Repository: Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit (language: python)

Issue:
DOC: Make sure all references to version TAG are correct
In `info.py`: `__version__ = 2.0.1`

PyPI: pip install pymialsrtk==2.0.1
Docker: docker pull sebastientourbier/mialsuperresolution:v2.0.2
Singularity: singularity pull library://tourbier/default/mialsuperresolutiontoolkit:v2.0.1
FIX: motion index computation
In `pymialsrtk/interfaces/preprocess.py` l.1006: prevent division by 0


Discussion / hints:

Fix patch:
diff --git a/.all-contributorsrc b/.all-contributorsrc
index 3668e6ce9..764196fae 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -59,7 +59,8 @@
       "profile": "https://github.com/helenelajous",
       "contributions": [
         "bug",
-        "test"
+        "test",
+        "review"
       ]
     },
     {
diff --git a/.circleci/config.yml b/.circleci/config.yml
index 6256f35fe..348473a3a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -117,7 +117,7 @@ jobs:
       - singularity/install-go:
           go-version: '1.13'
       - singularity/debian-install-3:
-          singularity-version: 3.5.1
+          singularity-version: 3.7.3
       - run:
           name: "Building Singularity image from Docker image"
           command: |
@@ -196,10 +196,12 @@ jobs:
                 --masks_derivatives_dir 'manual_masks'
 
             # Rename log
-            mv /tmp/src/mialsuperresolutiontoolkit/data/code/log.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_log.txt
+            mv /tmp/src/mialsuperresolutiontoolkit/data/code/log.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_log.txt
 
             # Rename partial coverage
-            mv /tmp/src/mialsuperresolutiontoolkit/data/code/coverage.xml /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_coverage.xml
+            mv /tmp/src/mialsuperresolutiontoolkit/data/code/coverage.xml \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_coverage.xml
       # - save_cache:
       #    key: ds-sample-derivatives-{{ .Branch }}-{{ epoch }}
       #    paths:
@@ -207,9 +209,16 @@ jobs:
       - run:
           name: "Test-01 - Checking outputs of MIALSRTK BIDS App run"
           command: |
-            # Get all files in derivatives except the _*.json interface hash generated by nipype (find) / remove the full path of the derivatives (sed) / sort the files and write it to a text file
-            sudo find /tmp/src/mialsuperresolutiontoolkit/data/derivatives -path */figures -prune -o -not -name "_*.json" -type f -print | sed s+/tmp/src/mialsuperresolutiontoolkit/data/derivatives/++ | sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_outputs.out
-            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-01_outputs.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_outputs.out
+            # Get all files in derivatives except the _*.json interface hash generated by nipype (find)
+            # remove the full path of the derivatives (sed) / sort the files and write it to a text file
+            sudo find /tmp/src/mialsuperresolutiontoolkit/data/derivatives \
+                -not -name "_*.json" \
+                -not -name "*.proc*" \
+                -type f -print | \
+                sed s+/tmp/src/mialsuperresolutiontoolkit/data/derivatives/++ | \
+                sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_outputs.out
+            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-01_outputs.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-01_outputs.out
             exit $?
       - run:
           name: "Test-02 - Run super-resolution pipelines on sample testing dataset with automatic brain extraction"
@@ -233,27 +242,43 @@ jobs:
                 /bids_dir /output_dir participant --participant_label 01 \
                 --param_file /bids_dir/code/participants_params.json \
                 --openmp_nb_of_cores 1 \
-                --nipype_nb_of_cores 1
+                --nipype_nb_of_cores 1 \
+                --profiling
 
             # Rename log
-            mv /tmp/src/mialsuperresolutiontoolkit/data/code/log.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_log.txt
+            mv /tmp/src/mialsuperresolutiontoolkit/data/code/log.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_log.txt
 
             # Rename partial coverage
-            mv /tmp/src/mialsuperresolutiontoolkit/data/code/coverage.xml /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_coverage.xml
+            mv /tmp/src/mialsuperresolutiontoolkit/data/code/coverage.xml \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_coverage.xml
       - run:
           name: "Test-02 - Checking outputs of MIALSRTK BIDS App run"
           command: |
-            # Get all files in derivatives except the _*.json interface hash generated by nipype (find) / remove the full path of the derivatives (sed) / sort the files and write it to a text file
-            sudo find /tmp/src/mialsuperresolutiontoolkit/data/derivatives -path */figures -prune -o -not -name "_*.json" -type f -print | sed s+/tmp/src/mialsuperresolutiontoolkit/data/derivatives/++ | sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_outputs.out
-            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-02_outputs.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_outputs.out
+            # Get all files in derivatives except the _*.json interface hash generated by nipype (find)
+            # remove the full path of the derivatives (sed) / sort the files and write it to a text file
+            sudo find /tmp/src/mialsuperresolutiontoolkit/data/derivatives \
+                -not -name "_*.json" \
+                -not -name "*.proc*" \
+                -type f -print | \
+                sed s+/tmp/src/mialsuperresolutiontoolkit/data/derivatives/++ | \
+                sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_outputs.out
+            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-02_outputs.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-02_outputs.out
             exit $?
       - run:
           name: "Clean working directory"
           when: always
           command: |
             sudo chown $(id -un):$(id -gn) -R /tmp/src/mialsuperresolutiontoolkit/data
-            find /tmp/src/mialsuperresolutiontoolkit/data/derivatives -not -name "*.svg" -not -name "*.png" -not -name "*.html"  -not -name "*.nii.gz" -not -name "*.rst" \
-                -not -name "*.mat" -not -name "*.gpickle" -not -name "*.lta" -not -name "*.json" -not -name "*.txt" -not -name "*.pklz" -type f -delete
+            find /tmp/src/mialsuperresolutiontoolkit/data/derivatives \
+                -not -name "*.svg" -not -name "*.png" \
+                -not -name "*.nii.gz" -not -name "*.rst" \
+                -not -name "*.mat" -not -name "*.gpickle" \
+                -not -name "*.lta" -not -name "*.json" \
+                -not -name "*.txt" -not -name "*.pklz" \
+                -not -name "*.log" -not -name "*.html" \
+                -type f -delete
       - persist_to_workspace:
           root: /tmp
           paths:
@@ -301,13 +326,14 @@ jobs:
       - singularity/install-go:
           go-version: '1.13'
       - singularity/debian-install-3:
-          singularity-version: 3.5.1
+          singularity-version: 3.7.3
       - run:
           name: "Create the data/test and data/singularity-derivatives folders"
           no_output_timeout: 1h
           command: |
             mkdir -p /tmp/src/mialsuperresolutiontoolkit/data/test
-            cp -R /tmp/src/mialsuperresolutiontoolkit/data/derivatives /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives
+            cp -R /tmp/src/mialsuperresolutiontoolkit/data/derivatives \
+                /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives
       - run:
           name: "Test-03 - Run super-resolution pipeline on sample testing dataset with manual masks"
           no_output_timeout: 6h
@@ -332,9 +358,16 @@ jobs:
       - run:
           name: "Test-03 - Checking outputs of MIALSRTK BIDS App run"
           command: |
-            # Get all files in derivatives except the _*.json interface hash generated by nipype (find) / remove the full path of the derivatives (sed) / sort the files and write it to a text file
-            sudo find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives -path */figures -prune -o -not -name "_*.json" -type f -print | sed s+/tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives/++ | sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-03_outputs.out
-            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-03_outputs.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-03_outputs.out
+            # Get all files in derivatives except the _*.json interface hash generated by nipype (find)
+            # remove the full path of the derivatives (sed) / sort the files and write it to a text file
+            sudo find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives \
+                -not -name "_*.json" \
+                -not -name "*.proc*" \
+                -type f -print | \
+                sed s+/tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives/++ | \
+                sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-03_outputs.out
+            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-03_outputs.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-03_outputs.out
             exit $?
       - run:
           name: "Test-04 - Run super-resolution pipelines on sample testing dataset with automatic brain extraction"
@@ -358,21 +391,35 @@ jobs:
                 /bids_dir /output_dir participant --participant_label 01 \
                 --param_file /bids_dir/code/participants_params.json \
                 --openmp_nb_of_cores 1 \
-                --nipype_nb_of_cores 1
+                --nipype_nb_of_cores 1 \
+                --profiling
       - run:
           name: "Test-04 - Checking outputs of MIALSRTK BIDS App run"
           command: |
-            # Get all files in derivatives except the _*.json interface hash generated by nipype (find) / remove the full path of the derivatives (sed) / sort the files and write it to a text file
-            sudo find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives -path */figures -prune -o -not -name "_*.json" -type f -print | sed s+/tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives/++ | sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-04_outputs.out
-            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-04_outputs.txt /tmp/src/mialsuperresolutiontoolkit/data/test/test-04_outputs.out
+            # Get all files in derivatives except the _*.json interface hash generated by nipype (find)
+            # remove the full path of the derivatives (sed) / sort the files and write it to a text file
+            sudo find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives \
+                -not -name "_*.json" \
+                -not -name "*.proc*" \
+                -type f -print | \
+                sed s+/tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives/++ | \
+                sort > /tmp/src/mialsuperresolutiontoolkit/data/test/test-04_outputs.out
+            diff /home/circleci/src/mialsuperresolutiontoolkit/.circleci/test-04_outputs.txt \
+                /tmp/src/mialsuperresolutiontoolkit/data/test/test-04_outputs.out
             exit $?
       - run:
           name: "Clean working directory"
           when: always
           command: |
             sudo chown $(id -un):$(id -gn) -R /tmp/src/mialsuperresolutiontoolkit/data
-            find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives -not -name "*.svg" -not -name "*.png" -not -name "*.html"  -not -name "*.nii.gz" -not -name "*.rst" \
-                -not -name "*.mat" -not -name "*.gpickle" -not -name "*.lta" -not -name "*.json" -not -name "*.txt" -not -name "*.pklz" -type f -delete
+            find /tmp/src/mialsuperresolutiontoolkit/data/singularity-derivatives \
+                -not -name "*.svg" -not -name "*.png" \
+                -not -name "*.nii.gz" -not -name "*.rst" \
+                -not -name "*.mat" -not -name "*.gpickle" \
+                -not -name "*.lta" -not -name "*.json" \
+                -not -name "*.txt" -not -name "*.pklz" \
+                -not -name "*.log" -not -name "*.html" \
+                -type f -delete
       - persist_to_workspace:
           root: /tmp
           paths:
diff --git a/README.md b/README.md
index bfc1a4529..d1ddd6864 100644
--- a/README.md
+++ b/README.md
@@ -43,21 +43,23 @@ The BIDS App of MIALSRTK has the following command line arguments:
                   [--param_file PARAM_FILE]
                   [--openmp_nb_of_cores OPENMP_NB_OF_CORES]
                   [--nipype_nb_of_cores NIPYPE_NB_OF_CORES]
+                  [--memory MEMORY]
                   [--masks_derivatives_dir MASKS_DERIVATIVES_DIR]
+                  [--profiling]
                   [-v]
                   bids_dir output_dir {participant}
-
-    Entrypoint script to the MIALSRTK BIDS App
+    
+    Argument parser of the MIALSRTK BIDS App
 
     positional arguments:
       bids_dir              The directory with the input dataset formatted
-                            according to the BIDS standard.
+                           according to the BIDS standard.
       output_dir            The directory where the output files should be stored.
-                            If you are running group level analysis this folder
-                            should be prepopulated with the results of
-                            theparticipant level analysis.
+                           If you are running group level analysis this folder
+                           should be prepopulated with the results of the
+                           participant level analysis.
       {participant}         Level of the analysis that will be performed. Only
-                            participant is available
+                           participant is available
 
     optional arguments:
       -h, --help            show this help message and exit
@@ -72,19 +74,30 @@ The BIDS App of MIALSRTK has the following command line arguments:
                             Path to a JSON file containing subjects' exams
                             information and super-resolution total variation
                             parameters.
-      --openmp_nb_of_cores  Specify number of cores used by OpenMP threads.
-                            Especially useful for NLM denoising and slice-to-volume
-                            registration.
-                            (Default: 0, meaning it will be determined automatically)      
+      --openmp_nb_of_cores OPENMP_NB_OF_CORES
+                            Specify number of cores used by OpenMP threads
+                            Especially useful for NLM denoising and slice-to-
+                            volume registration. (Default: 0, meaning it will be
+                            determined automatically)
+      --nipype_nb_of_cores NIPYPE_NB_OF_CORES
                             Specify number of cores used by the Niype workflow
                             library to distribute the execution of independent
-                            processing workflow nodes (i.e. interfaces).
-      --nipype_nb_of_cores  Especially useful in the case of slice-by-slice bias
+                            processing workflow nodes (i.e. interfaces)
+                            (Especially useful in the case of slice-by-slice bias
                             field correction and intensity standardization steps
-                            for example.
-                            (Default: 0, meaning it will be determined automatically)
-      --masks_derivatives_dir
-                            Use manual brain masks found in the specified directory
+                            for example). (Default: 0, meaning it will be
+                            determined automatically)
+      --memory MEMORY       Limit the workflow to using the amount of specified
+                            memory [in gb] (Default: 0, the workflow memory
+                            consumption is not limited)
+      --masks_derivatives_dir MASKS_DERIVATIVES_DIR
+                            Use manual brain masks found in
+                            ``<output_dir>/<masks_derivatives_dir>/ directory``
+                            directory
+      --profiling           Save node runtime statistics (processing times, number
+                            of threads, memory) in a JSON-style `run_stats.log`
+                            and in the form of a gantt chart in a HTML
+                            `run_stats.log.html`
       -v, --version         show program's version number and exit
 
 ## Credits 
diff --git a/build_bidsapp.sh b/build_bidsapp.sh
index 91fcaf07b..38446c5b6 100644
--- a/build_bidsapp.sh
+++ b/build_bidsapp.sh
@@ -1,4 +1,4 @@
-#! /bin/sh
+#!/bin/sh
 CMP_BUILD_DATE="$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")"
 echo "$CMP_BUILD_DATE"
 
diff --git a/docker/bidsapp/Dockerfile b/docker/bidsapp/Dockerfile
index 4ea8f149e..7384fb701 100644
--- a/docker/bidsapp/Dockerfile
+++ b/docker/bidsapp/Dockerfile
@@ -51,13 +51,13 @@ RUN /bin/bash -c 'ls -al && $CONDA_ACTIVATE && pip install .'
 WORKDIR /app
 
 ##############################################################
-# Use tcmalloc instead of malloc in TensorFLow
+# Use jemalloc instead of malloc in TensorFLow
 # that suffers less from fragmentation when
 # allocating and deallocating large objects
 ##############################################################
-RUN apt-get update && apt-get install -y google-perftools && \
+RUN apt-get update && apt-get install -y libjemalloc-dev && \
     rm -rf /var/lib/apt/lists/*
-ENV LD_PRELOAD=/usr/lib/libtcmalloc.so.4
+ENV LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so
 
 ##############################################################
 # Create entrypoint scripts
diff --git a/docker/bidsapp/environment.yml b/docker/bidsapp/environment.yml
index 1f093554e..4c5710f2a 100644
--- a/docker/bidsapp/environment.yml
+++ b/docker/bidsapp/environment.yml
@@ -6,27 +6,28 @@ channels:
 - anaconda
 
 dependencies:
-- python=3.6.8
-- pip=19.0.3
+- python=3.7.10
+- pip=21.0.1
 - nibabel=3.0.1
-- nipype=1.5.1
+- pandas=1.2.3
+- nipype=1.6.0
 - traits=5.1.2
-- traitsui=6.0.0
-- pyface=7.0.0
+- nilearn=0.7.1
 - pydotplus=2.0.2
 - pandoc=2.11.0.1
+- seaborn=0.11.1
 
 - pip:
     - coverage==5.1
     - duecredit==0.8.0
     - apptools==4.4.0
-    # - niworkflows==0.10.2
     - graphviz
-    - numpy==1.16.4 # numpy 1.18.5 caused error (fix: https://github.com/DeepLabCut/Docker4DeepLabCut2.0/issues/26)
-    - tensorflow==1.13.1 # https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp36-cp36m-linux_x86_64.whl # tensorflow==1.15 # tflearn needs tensorflow 1.0+
-    - tensorflow-estimator==1.13.0
-    - tensorboard==1.13.1
-    - tflearn==0.3.2
+    - numpy==1.16.6
+    # - niworkflows==0.10.2
+    - tensorflow==2.3.0
+    - tensorflow-estimator==2.3.0
+    - tensorboard==2.3.0
+    - tflearn==0.5.0
     - MedPy==0.4.0
     - opencv-python==4.1.0.25
     - scikit-learn==0.20.3
@@ -36,3 +37,4 @@ dependencies:
     - sphinx-argparse==0.2.5
     - nbsphinx==0.7.1
     - recommonmark==0.5.0
+    - matplotlib==3.4
diff --git a/docker/bidsapp/run.py b/docker/bidsapp/run.py
index f500c8ffe..f9e9a32aa 100644
--- a/docker/bidsapp/run.py
+++ b/docker/bidsapp/run.py
@@ -1,4 +1,4 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
@@ -31,17 +31,17 @@ def return_default_nb_of_cores(nb_of_cores, openmp_proportion=2):
 
     Parameters
     ----------
-    nb_of_cores <int>
+    nb_of_cores : int
         Number of cores available on the computer
-    openmp_proportion <int>
+    openmp_proportion : int
         Proportion of cores dedicated to OpenMP threads
 
     Returns
     -------
-    openmp_nb_of_cores <int>
+    openmp_nb_of_cores : int
         Number of cores used by default by openmp
 
-    nipype_nb_of_cores <int>
+    nipype_nb_of_cores : int
         Number of cores used by default by openmp
     """
     openmp_nb_of_cores = nb_of_cores // openmp_proportion
@@ -63,81 +63,90 @@ def check_and_return_valid_nb_of_cores(openmp_nb_of_cores, nipype_nb_of_cores, o
 
     Parameters
     ----------
-    openmp_nb_of_cores <int>
+    openmp_nb_of_cores : int
         Number of cores used by openmp that was initially set
 
-    nipype_nb_of_cores <int>
+    nipype_nb_of_cores : int
         Number of cores used by Niype that was initially set
 
     Returns
     -------
-    openmp_nb_of_cores <int>
+    openmp_nb_of_cores : int
         Valid number of cores used by openmp
 
-    nipype_nb_of_cores <int>
+    nipype_nb_of_cores : int
         Valid number of cores used by Niype
     """
     nb_of_cores = multiprocessing.cpu_count()
 
-    # Handles all the scenari for values of openmp_nb_of_cores and nipype_nb_of_cores
-    # and make the correction if needed.
-    if openmp_nb_of_cores == 0 and nipype_nb_of_cores == 0:
-
-        openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
-
-    elif openmp_nb_of_cores > 0 and nipype_nb_of_cores == 0:
+    # Handles all the scenarios for values of nb_of_cores, openmp_nb_of_cores
+    # and nipype_nb_of_cores and make the correction if needed.
+    if nb_of_cores == 1:
+        openmp_nb_of_cores = 1
+        nipype_nb_of_cores = 1
+    else:
+        if openmp_nb_of_cores == 0 and nipype_nb_of_cores == 0:
+            openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
 
-        if openmp_nb_of_cores >= nb_of_cores:
+        elif openmp_nb_of_cores > 0 and nipype_nb_of_cores == 0:
             if openmp_nb_of_cores > nb_of_cores:
                 print(f'WARNING: Value of {openmp_nb_of_cores} set by "--openmp_nb_of_cores" is bigger than'
                       f'the number of cores available ({nb_of_cores}) and will be reset.')
-            openmp_nb_of_cores = nb_of_cores
-            nipype_nb_of_cores = 1
-        else:
-          openmp_nb_of_cores = openmp_nb_of_cores
-          nipype_nb_of_cores = nb_of_cores // openmp_nb_of_cores
-
-    elif openmp_nb_of_cores == 0 and nipype_nb_of_cores > 0:
+                openmp_nb_of_cores = nb_of_cores
+                nipype_nb_of_cores = 1
+            else:
+              openmp_nb_of_cores = openmp_nb_of_cores
+              nipype_nb_of_cores = nb_of_cores // openmp_nb_of_cores
 
-        if nipype_nb_of_cores >= nb_of_cores:
+        elif openmp_nb_of_cores == 0 and nipype_nb_of_cores > 0:
             if nipype_nb_of_cores > nb_of_cores:
                 print(f'WARNING: Value of {nipype_nb_of_cores} set by "--nipype_nb_of_cores" is bigger than'
                       f'the number of cores available ({nb_of_cores}) and will be reset.')
-            nipype_nb_of_cores = nb_of_cores
-            openmp_nb_of_cores = 1
-        else:
-          nipype_nb_of_cores = nipype_nb_of_cores
-          openmp_nb_of_cores = nb_of_cores // nipype_nb_of_cores
-
-    elif openmp_nb_of_cores > 0 and nipype_nb_of_cores > 0:
-
-        if nipype_nb_of_cores >= nb_of_cores:
-            if openmp_nb_of_cores >= nb_of_cores:
-                print(f'WARNING: Value of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--nipype_nb_of_cores" and'
-                      f'"--nipype_nb_of_cores" when multiplied are bigger than the number of cores available ({nb_of_cores})'
-                      'and will be reset.')
-                openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
+                nipype_nb_of_cores = nb_of_cores
+                openmp_nb_of_cores = 1
             else:
-                if (openmp_nb_of_cores * nipype_nb_of_cores) > nb_of_cores:
-                    print(f'WARNING: Multiplication of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--nipype_nb_of_cores" and'
-                          f'"--nipype_nb_of_cores" are bigger than the number of cores available ({nb_of_cores}) and will be reset.')
+              nipype_nb_of_cores = nipype_nb_of_cores
+              openmp_nb_of_cores = nb_of_cores // nipype_nb_of_cores
+
+        elif openmp_nb_of_cores > 0 and nipype_nb_of_cores > 0:
+            if nipype_nb_of_cores > nb_of_cores:
+                if openmp_nb_of_cores > nb_of_cores:
+                    print(f'WARNING: Value of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--openmp_nb_of_cores" and'
+                          f'"--nipype_nb_of_cores" are both bigger than the number of cores available ({nb_of_cores})'
+                          'and will be reset.')
                     openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
-        else:
-            if openmp_nb_of_cores >= nb_of_cores:
-                print(f'WARNING: Value of {openmp_nb_of_cores} set by "--nipype_nb_of_cores" are bigger'
-                      f'than the number of cores available ({nb_of_cores}) and will be reset.')
-                openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
+                else:
+                    if (openmp_nb_of_cores * nipype_nb_of_cores) > nb_of_cores:
+                        print(f'WARNING: Multiplication of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--nipype_nb_of_cores" and'
+                              f'"--nipype_nb_of_cores" is bigger than the number of cores available ({nb_of_cores}) and will be reset.')
+                        openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
             else:
-                if (openmp_nb_of_cores * nipype_nb_of_cores) > nb_of_cores:
-                    print(f'WARNING: Multiplication of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--nipype_nb_of_cores" and'
-                          f'"--nipype_nb_of_cores" are bigger than the number of cores available ({nb_of_cores}) and will be reset.')
+                if openmp_nb_of_cores > nb_of_cores:
+                    print(f'WARNING: Value of {openmp_nb_of_cores} set by "--openmp_nb_of_cores" is bigger'
+                          f'than the number of cores available ({nb_of_cores}) and will be reset.')
                     openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
+                else:
+                    if (openmp_nb_of_cores * nipype_nb_of_cores) > nb_of_cores:
+                        print(f'WARNING: Multiplication of {nipype_nb_of_cores} and {openmp_nb_of_cores} set by "--nipype_nb_of_cores" and'
+                              f'"--nipype_nb_of_cores" is bigger than the number of cores available ({nb_of_cores}) and will be reset.')
+                        openmp_nb_of_cores, nipype_nb_of_cores = return_default_nb_of_cores(nb_of_cores, openmp_proportion)
 
     return openmp_nb_of_cores, nipype_nb_of_cores
 
 
-def main(bids_dir, output_dir, subject, p_stacks, session, paramTV=None, number_of_cores=1, srID=None,
-         masks_derivatives_dir='', dict_custom_interfaces=None): #skip_svr=False, do_refine_hr_mask=False, skip_nlm_denoising=False, skip_stacks_ordering=False):
+def main(bids_dir, output_dir,
+         subject,
+         session,
+         p_stacks,
+         paramTV=None,
+         srID=None,
+         masks_derivatives_dir='',
+         masks_desc=None,
+         dict_custom_interfaces=None,
+         nipype_number_of_cores=1,
+         openmp_number_of_cores=1,
+         memory=0,
+         save_profiler_log=False):
     """Main function that creates and executes the workflow of the BIDS App on one subject.
 
     It creates an instance of the class :class:`pymialsrtk.pipelines.anatomical.srr.AnatomicalPipeline`,
@@ -145,45 +154,49 @@ def main(bids_dir, output_dir, subject, p_stacks, session, paramTV=None, number_
 
     Parameters
     ----------
-    bids_dir <string>
+    bids_dir : string
         BIDS root directory (required)
 
-    output_dir <string>
+    output_dir : string
         Output derivatives directory (required)
 
-    subject <string>
+    subject : string
         Subject ID (in the form ``sub-XX``)
 
-    p_stacks list<<int>>
-        List of stack to be used in the reconstruction. The specified order is kept if `skip_stacks_ordering` is True.
-
-    session <string>
+    session : string
         Session ID if applicable (in the form ``ses-YY``)
 
-    paramTV dict <'deltatTV': float, 'lambdaTV': float, 'primal_dual_loops': int>>
-        Dictionary of Total-Variation super-resolution optimizer parameters
+    p_stacks : list(int)
+        List of stack to be used in the reconstruction. The specified order is kept if `skip_stacks_ordering` is True.
 
-    number_of_cores <int>
-        Number of cores / CPUs used by the Nipype worflow execution engine
+    paramTV dict : {'deltatTV': float, 'lambdaTV': float, 'primal_dual_loops': int}
+        Dictionary of Total-Variation super-resolution optimizer parameters
 
-    srID <string>
+    srID : string
         ID of the reconstruction useful to distinguish when multiple reconstructions
         with different order of stacks are run on the same subject
 
-    masks_derivatives_dir <string>
+    masks_derivatives_dir : string
         directory basename in BIDS directory derivatives where to search for masks (optional)
 
-    skip_svr <bool> (optional)
-        Weither the Slice-to-Volume Registration should be skipped in the image reconstruction. (default is False)
+    masks_desc : string
+        BIDS description tag of masks to use (optional)
+
+    dict_custom_interfaces : {'do_refine_hr_mask': False, 'skip_nlm_denoising': False, 'skip_stacks_ordering': False}
+        Dictionary that customize the workflow (skip interfaces).
 
-    do_refine_hr_mask <bool> (optional)
-        Weither a refinement of the HR mask should be performed. (default is False)
+    nipype_number_of_cores : int
+        Number of cores / CPUs used by the Nipype worflow execution engine
+
+    openmp_number_of_cores : int
+        Number of threads used by OpenMP
 
-    skip_nlm_denoising <bool> (optional)
-        Weither the NLM denoising preprocessing should be skipped. (default is False)
+    memory : int
+        Maximal amount of memory used by the workflow
+        (Default: 0, workflow uses all available memory)
 
-    skip_stacks_ordering <bool> (optional)
-        Weither the automatic stacks ordering should be skipped. (default is False)
+    save_profiler_log : bool (optional)
+        Save the node profiler (runtime stats) log. (default is False)
 
     """
 
@@ -196,6 +209,7 @@ def main(bids_dir, output_dir, subject, p_stacks, session, paramTV=None, number_
 
     if srID is None:
         srID = "01"
+
     # Initialize an instance of AnatomicalPipeline
     pipeline = AnatomicalPipeline(bids_dir,
                                   output_dir,
@@ -205,24 +219,25 @@ def main(bids_dir, output_dir, subject, p_stacks, session, paramTV=None, number_
                                   session,
                                   paramTV,
                                   masks_derivatives_dir,
-                                  p_dict_custom_interfaces=dict_custom_interfaces)
-                                  # skip_svr,
-                                  # do_refine_hr_mask,
-                                  # p_skip_nlm_denoising=skip_nlm_denoising,
-                                  # p_skip_stacks_ordering=skip_stacks_ordering)
+                                  masks_desc,
+                                  p_dict_custom_interfaces=dict_custom_interfaces,
+                                  openmp_number_of_cores=openmp_number_of_cores,
+                                  nipype_number_of_cores=nipype_number_of_cores
+                                  )
+
     # Create the super resolution Nipype workflow
-    pipeline.create_workflow()
+    pipeline.create_workflow(save_profiler_log=save_profiler_log)
 
     # Execute the workflow
-    res = pipeline.run(number_of_cores=number_of_cores)
+    res = pipeline.run(number_of_cores=nipype_number_of_cores,
+                       memory=memory,
+                       save_profiler_log=save_profiler_log)
 
     return res
 
 
 if __name__ == '__main__':
 
-    bids_dir = os.path.join('/fetaldata')
-
     parser = get_parser()
     args = parser.parse_args()
 
@@ -256,24 +271,31 @@ def main(bids_dir, output_dir, subject, p_stacks, session, paramTV=None, number_
                     ses = sr_params["session"] if "session" in sr_params.keys() else None
                     stacks = sr_params['stacks'] if 'stacks' in sr_params.keys() else None
                     paramTV = sr_params['paramTV'] if 'paramTV' in sr_params.keys() else None
+                    masks_desc = sr_params['masks_desc'] if 'masks_desc' in sr_params.keys() else None
 
                     dict_custom_interfaces = sr_params['custom_interfaces'] if 'custom_interfaces' in sr_params.keys() else None
 
-                    if ("sr-id" not in sr_params.keys()):
+                    if "sr-id" not in sr_params.keys():
                         print('Do not process subjects %s because of missing parameters.' % sub)
                         continue
 
                     res = main(bids_dir=args.bids_dir,
                                output_dir=args.output_dir,
                                subject=sub,
-                               p_stacks=stacks,
                                session=ses,
+                               p_stacks=stacks,
                                paramTV=paramTV,
                                srID=sr_params['sr-id'],
                                masks_derivatives_dir=args.masks_derivatives_dir,
-                               number_of_cores=nipype_nb_of_cores,
-                               dict_custom_interfaces = dict_custom_interfaces)
-
+                               masks_desc=masks_desc,
+                               dict_custom_interfaces=dict_custom_interfaces,
+                               nipype_number_of_cores=nipype_nb_of_cores,
+                               openmp_number_of_cores=openmp_nb_of_cores,
+                               memory=args.memory,
+                               save_profiler_log=args.profiling)
     else:
-        print('ERROR: Processing of all dataset not implemented yet\n At least one participant label should be provided')
+        print(
+            'ERROR: Processing of all dataset not implemented yet\n '
+            'At least one participant label should be provided'
+        )
         sys.exit(2)
diff --git a/docker/jupyter/environment.yml b/docker/jupyter/environment.yml
index 925ceaa64..6a6ac6531 100644
--- a/docker/jupyter/environment.yml
+++ b/docker/jupyter/environment.yml
@@ -43,3 +43,4 @@ dependencies:
     - recommonmark==0.5.0
     - jupyter_contrib_nbextensions #==0.5.1
 #    - pybids==0.9.1
+    - matplotlib==3.4
diff --git a/documentation/_static/run_stats.log.html b/documentation/_static/run_stats.log.html
new file mode 100644
index 000000000..796e2a3ec
--- /dev/null
+++ b/documentation/_static/run_stats.log.html
@@ -0,0 +1,1253 @@
+<!DOCTYPE html>
+    <head>
+        <style>
+            #content{
+                width:99%;
+                height:100%;
+                position:absolute;
+            }
+            .node{
+                background-color:#7070FF;
+                border-radius: 5px;
+                position:absolute;
+                width:20px;
+                white-space:pre-wrap;
+            }
+            .line{
+                position: absolute;
+                color: #C2C2C2;
+                opacity: 0.5;
+                margin: 0px;
+            }
+            .time{
+                position: absolute;
+                font-size: 16px;
+                color: #666666;
+                margin: 0px;
+            }
+            .bar{
+                position: absolute;
+                height: 1px;
+                opacity: 0.7;
+            }
+            .dot{
+                position: absolute;
+                width: 1px;
+                height: 1px;
+                background-color: red;
+            }
+            .label {
+                width:20px;
+                height:20px;
+                opacity: 0.7;
+                display: inline-block;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="content">
+            <div style="display:inline-block;">
+    <p>Start: 2021-03-23 21:45:18</p><p>Finish: 2021-03-23 22:00:58</p><p>Duration: 15.66 minutes</p><p>Nodes: 45</p><p>Cores: 1</p>
+    </div>
+    <div style="display:inline-block;margin-left:60px;vertical-align: top;">
+        <p><span><div class="label" style="background-color:#90BBD7;"></div> Estimated Resource</span></p>
+        <p><span><div class="label" style="background-color:#03969D;"></div> Actual Resource</span></p>
+        <p><span><div class="label" style="background-color:#f00;"></div> Failed Node</span></p>
+    </div>
+    <hr class='line' width='98%' style='top:220px;'><p class='time' style='top:200px;'> 21:45 </p><hr class='line' width='98%' style='top:720px;'><p class='time' style='top:700px;'> 21:55 </p><hr class='line' width='98%' style='top:1220px;'><p class='time' style='top:1200px;'> 22:05 </p><div class='node' style='left:60px;top:220.0px;height:3px;background-color:#7070FF;'title='data_grabber
+duration:0.0034372166666666666
+start:2021-03-23 21:45:18
+end:2021-03-23 21:45:19'></div><div class='node' style='left:60px;top:221.70743166666668px;height:3px;background-color:#9B9BFF;'title='t2ws_filter_prior_masks
+duration:0.0033797666666666665
+start:2021-03-23 21:45:20
+end:2021-03-23 21:45:21'></div><div class='node' style='left:60px;top:224.9904925px;height:3.3564716666666667px;background-color:#4E4EB2;'title='_brainExtraction0
+duration:0.10712943333333333
+start:2021-03-23 21:45:24
+end:2021-03-23 21:45:31'></div><div class='node' style='left:60px;top:231.66871666666665px;height:3.100386666666666px;background-color:#4E4EB2;'title='_brainExtraction1
+duration:0.10200773333333334
+start:2021-03-23 21:45:32
+end:2021-03-23 21:45:39'></div><div class='node' style='left:60px;top:238.3402725px;height:3.151486666666667px;background-color:#9B9BFF;'title='_brainExtraction2
+duration:0.10302973333333335
+start:2021-03-23 21:45:40
+end:2021-03-23 21:45:47'></div><div class='node' style='left:60px;top:246.68153166666667px;height:3px;background-color:#7070FF;'title='stackOrdering
+duration:0.009143033333333333
+start:2021-03-23 21:45:50
+end:2021-03-23 21:45:51'></div><div class='node' style='left:60px;top:248.35257833333333px;height:3px;background-color:#2D2D66;'title='filenames_gen
+duration:0.003378
+start:2021-03-23 21:45:52
+end:2021-03-23 21:45:53'></div><div class='node' style='left:60px;top:250.02389583333334px;height:3px;background-color:#4E4EB2;'title='masks_filtered
+duration:0.0033728499999999997
+start:2021-03-23 21:45:54
+end:2021-03-23 21:45:55'></div><div class='node' style='left:60px;top:251.6922575px;height:3px;background-color:#7070FF;'title='t2ws_filtered
+duration:0.0033749166666666667
+start:2021-03-23 21:45:56
+end:2021-03-23 21:45:57'></div><div class='node' style='left:60px;top:255.02110583333334px;height:3px;background-color:#7070FF;'title='_srtkCorrectSliceIntensity010
+duration:0.009364866666666666
+start:2021-03-23 21:46:00
+end:2021-03-23 21:46:01'></div><div class='node' style='left:60px;top:256.6997733333333px;height:3px;background-color:#7070FF;'title='_srtkCorrectSliceIntensity011
+duration:0.009384916666666666
+start:2021-03-23 21:46:02
+end:2021-03-23 21:46:03'></div><div class='node' style='left:60px;top:258.3672925px;height:3px;background-color:#9B9BFF;'title='_srtkCorrectSliceIntensity012
+duration:0.009956033333333333
+start:2021-03-23 21:46:04
+end:2021-03-23 21:46:05'></div><div class='node' style='left:60px;top:261.69869166666666px;height:3px;background-color:#9B9BFF;'title='_nlmDenoise0
+duration:0.05480828333333333
+start:2021-03-23 21:46:08
+end:2021-03-23 21:46:12'></div><div class='node' style='left:60px;top:265.0419591666667px;height:3px;background-color:#4E4EB2;'title='_nlmDenoise1
+duration:0.049114783333333335
+start:2021-03-23 21:46:12
+end:2021-03-23 21:46:15'></div><div class='node' style='left:60px;top:268.37549916666666px;height:3px;background-color:#4E4EB2;'title='_nlmDenoise2
+duration:0.057814366666666665
+start:2021-03-23 21:46:16
+end:2021-03-23 21:46:20'></div><div class='node' style='left:60px;top:275.04323416666665px;height:3px;background-color:#7070FF;'title='_srtkCorrectSliceIntensity01_nlm0
+duration:0.009348733333333333
+start:2021-03-23 21:46:24
+end:2021-03-23 21:46:25'></div><div class='node' style='left:60px;top:276.71719083333335px;height:3px;background-color:#7070FF;'title='_srtkCorrectSliceIntensity01_nlm1
+duration:0.0094369
+start:2021-03-23 21:46:26
+end:2021-03-23 21:46:27'></div><div class='node' style='left:60px;top:278.38663583333334px;height:3px;background-color:#4E4EB2;'title='_srtkCorrectSliceIntensity01_nlm2
+duration:0.00937845
+start:2021-03-23 21:46:28
+end:2021-03-23 21:46:29'></div><div class='node' style='left:60px;top:283.38576916666665px;height:3px;background-color:#7070FF;'title='_srtkSliceBySliceN4BiasFieldCorrection0
+duration:0.04807218333333333
+start:2021-03-23 21:46:34
+end:2021-03-23 21:46:37'></div><div class='node' style='left:60px;top:286.72736px;height:3px;background-color:#4E4EB2;'title='_srtkSliceBySliceN4BiasFieldCorrection1
+duration:0.04658175
+start:2021-03-23 21:46:38
+end:2021-03-23 21:46:41'></div><div class='node' style='left:60px;top:290.06477083333334px;height:3px;background-color:#2D2D66;'title='_srtkSliceBySliceN4BiasFieldCorrection2
+duration:0.048972383333333334
+start:2021-03-23 21:46:42
+end:2021-03-23 21:46:45'></div><div class='node' style='left:60px;top:296.7328358333333px;height:3px;background-color:#9B9BFF;'title='_srtkCorrectSliceIntensity02_nlm0
+duration:0.009414116666666666
+start:2021-03-23 21:46:50
+end:2021-03-23 21:46:51'></div><div class='node' style='left:60px;top:298.40654416666666px;height:3px;background-color:#4E4EB2;'title='_srtkCorrectSliceIntensity02_nlm1
+duration:0.009218166666666666
+start:2021-03-23 21:46:52
+end:2021-03-23 21:46:53'></div><div class='node' style='left:60px;top:300.0747958333333px;height:3px;background-color:#4E4EB2;'title='_srtkCorrectSliceIntensity02_nlm2
+duration:0.009360966666666666
+start:2021-03-23 21:46:54
+end:2021-03-23 21:46:55'></div><div class='node' style='left:60px;top:303.4110075px;height:3px;background-color:#4E4EB2;'title='srtkIntensityStandardization01_nlm
+duration:0.011908683333333333
+start:2021-03-23 21:46:58
+end:2021-03-23 21:46:59'></div><div class='node' style='left:60px;top:305.0870275px;height:3px;background-color:#4E4EB2;'title='srtkHistogramNormalization_nlm
+duration:0.03338223333333334
+start:2021-03-23 21:47:00
+end:2021-03-23 21:47:02'></div><div class='node' style='left:60px;top:308.42059083333334px;height:3px;background-color:#2D2D66;'title='srtkIntensityStandardization02_nlm
+duration:0.010578416666666667
+start:2021-03-23 21:47:04
+end:2021-03-23 21:47:05'></div><div class='node' style='left:60px;top:310.09629583333333px;height:3px;background-color:#9B9BFF;'title='_srtkSliceBySliceCorrectBiasField0
+duration:0.009391816666666667
+start:2021-03-23 21:47:06
+end:2021-03-23 21:47:07'></div><div class='node' style='left:60px;top:311.7563108333333px;height:3px;background-color:#7070FF;'title='_srtkSliceBySliceCorrectBiasField1
+duration:0.009245233333333333
+start:2021-03-23 21:47:08
+end:2021-03-23 21:47:09'></div><div class='node' style='left:60px;top:313.4237175px;height:3px;background-color:#7070FF;'title='_srtkSliceBySliceCorrectBiasField2
+duration:0.009225866666666667
+start:2021-03-23 21:47:10
+end:2021-03-23 21:47:11'></div><div class='node' style='left:60px;top:316.76351px;height:3px;background-color:#4E4EB2;'title='_srtkMaskImage010
+duration:0.008166516666666667
+start:2021-03-23 21:47:14
+end:2021-03-23 21:47:15'></div><div class='node' style='left:60px;top:318.42675916666667px;height:3px;background-color:#9B9BFF;'title='_srtkMaskImage011
+duration:0.008194483333333334
+start:2021-03-23 21:47:16
+end:2021-03-23 21:47:17'></div><div class='node' style='left:60px;top:320.0985191666667px;height:3px;background-color:#9B9BFF;'title='_srtkMaskImage012
+duration:0.008187999999999999
+start:2021-03-23 21:47:18
+end:2021-03-23 21:47:19'></div><div class='node' style='left:60px;top:323.43388416666664px;height:583.971815px;background-color:#7070FF;'title='srtkImageReconstruction
+duration:11.7194363
+start:2021-03-23 21:47:22
+end:2021-03-23 21:59:06'></div><div class='node' style='left:60px;top:910.6860983333332px;height:3px;background-color:#2D2D66;'title='_srtkCorrectSliceIntensity020
+duration:0.009540433333333332
+start:2021-03-23 21:59:07
+end:2021-03-23 21:59:08'></div><div class='node' style='left:60px;top:912.341265px;height:3px;background-color:#9B9BFF;'title='_srtkCorrectSliceIntensity021
+duration:0.009386383333333333
+start:2021-03-23 21:59:09
+end:2021-03-23 21:59:10'></div><div class='node' style='left:60px;top:914.0104674999999px;height:3px;background-color:#7070FF;'title='_srtkCorrectSliceIntensity022
+duration:0.009122966666666668
+start:2021-03-23 21:59:11
+end:2021-03-23 21:59:12'></div><div class='node' style='left:60px;top:917.3449291666667px;height:3px;background-color:#9B9BFF;'title='srtkIntensityStandardization01
+duration:0.0122605
+start:2021-03-23 21:59:15
+end:2021-03-23 21:59:16'></div><div class='node' style='left:60px;top:919.0189991666666px;height:3px;background-color:#9B9BFF;'title='srtkHistogramNormalization
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+start:2021-03-23 22:00:44
+end:2021-03-23 22:00:45'></div><div class='bar' style='background-color:#03969D;height:5.118px;width:199.013px;left:150px; top:992.424px;'title='967 threads
+duration:0.142
+start:2021-03-23 22:00:45
+end:2021-03-23 22:00:54'></div><div class='bar' style='background-color:#03969D;height:3.000px;width:0.000px;left:150px; top:999.542px;'title='0 threads
+duration:0.025
+start:2021-03-23 22:00:54
+end:2021-03-23 22:00:55'></div><div class='bar' style='background-color:#03969D;height:3.000px;width:12.770px;left:150px; top:1000.777px;'title='62 threads
+duration:0.004
+start:2021-03-23 22:00:55
+end:2021-03-23 22:00:56'></div><div class='bar' style='background-color:#03969D;height:3.000px;width:0.000px;left:150px; top:1000.958px;'title='0 threads
+duration:0.029
+start:2021-03-23 22:00:56
+end:2021-03-23 22:00:57'></div><div class='bar' style='background-color:#03969D;height:3.000px;width:200.000px;left:150px; top:1002.431px;'title='972 threads
+duration:0.016
+start:2021-03-23 22:00:57
+end:2021-03-23 22:00:58'></div><div class='bar' style='background-color:#03969D;height:3.000px;width:0.000px;left:150px; top:1003.224px;'title='0 threads
+duration:0.000
+start:2021-03-23 22:00:58
+end:2021-03-23 22:00:58'></div>
+        </div>
+    </body>
\ No newline at end of file
diff --git a/documentation/changes.rst b/documentation/changes.rst
index 3eeeaa6c2..8fca11193 100644
--- a/documentation/changes.rst
+++ b/documentation/changes.rst
@@ -2,12 +2,85 @@
 Changes
 **************
 
+
+Version 2.0.2
+--------------
+
+Date: August 23, 2021
+
+This corresponds to the release of MIAL Super-Resolution Toolkit 2.0.2,
+that includes in particular the following changes.
+
+New feature
+=============
+
+- ``pymialsrtk`` enables to monitor resources (time, threads, memory) used by the
+  interface of the pipeline at execution with the ``--profiling`` option flag.
+  This includes the creation of a new module `pymialsrtk.utils.monitoring` that
+  includes all methods originating from Nipype and modified to support this task
+  (See `pull request 92 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/92>`_).
+
+- ``pymialsrtk`` enables to fix the maximal amount of memory (in Gb) that could be used by the
+  pipelines at execution with the ``--memory MEMORY_Gb`` option flag.
+  (See `pull request 92 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/92>`_).
+
+- ``pymialsrtk`` generates a HTML processing report for each subject in `sub-<label>/report/sub-<label>.html`.
+  It includes the following:
+    - Pipeline/workflow configuration summary
+    - Nipype workflow execution graph
+    - Links to the log and the profiling output report
+    - Plots for the quality check of the automatic reordering step based on the motion index.
+    - Three orthogonal cuts of the reconstructed image
+    - Computing environment summary
+  (See pull requests `97 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/97>`_, `102 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/102>`_, and `103 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/103>`_).
+
+Python update
+===============
+
+* From `3.6.8` to `3.7.10`
+
+New package
+==============
+
+* pandas `1.2.3`
+
+Package update
+===============
+
+* pip from `19.0.3` to `21.0.1`
+* numpy from `1.16.4` to `1.16.6`
+* tflearn from `0.3.2` to `0.5.0`
+* tensorflow from `1.13.1` to `2.3.0`
+* tensorflow-estimator from `1.13.0` to `2.3.0`
+* tensorboard from `1.13.1` to `2.3.0`
+
+Bug fix
+========
+
+* Correct the output filename of the high-resolution brain mask sunk
+  in ``mialsrtk-<variant>/sub-<label>/anat``
+  (See `pull request 92 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/92>`_).
+
+Software development life cycle
+================================
+
+* Add `--profiling` option flag to test-02 and test-04
+  (See `pull request 92 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/92>`_).
+
+More...
+========
+
+Please check `pull request 70 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/70>`_
+for more change details and development discussions.
+
+
 Version 2.0.1
 --------------
 
 Date: December 24, 2020
 
-This corresponds to the release of MIAL Super-Resolution Toolkit 2.0.1, that includes in particular the following changes.
+This corresponds to the release of MIAL Super-Resolution Toolkit 2.0.1,
+that includes in particular the following changes.
 
 Major change
 =============
@@ -25,7 +98,6 @@ New feature
 * Support of Singularity to execute MIALSTK on high-performance computing cluster (See `pull request 60 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/60>`_).
 * ``pymialsrtk`` implements for convenience a Python wrapper that generates the Singularity command line of the BIDS App for you, prints it out for reporting purposes, and then executes it without further action needed (See `pull request 61 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/61>`_).
 
-
 Software development life cycle
 ================================
 
@@ -36,7 +108,6 @@ Software development life cycle
 	* Test 03: Run BIDS App on the sample `data/` BIDS dataset with the ``--manual_masks`` option without code coverage.
 	* Test 04: Run BIDS App on the sample `data/` BIDS dataset with automated brain extraction (masking) without code coverage.
 
-
 More...
 ========
 
@@ -50,19 +121,14 @@ Date: November 25, 2020
 
 This corresponds to the first release of the second version of the MIAL Super-Resolution Toolkit, which has evolved massively over the last years in terms of the underlying codebase and the scope of the functionality provided, following recent advances in standardization of neuroimaging data organization and processing workflows.
 
-
 Major changes
 =============
 
 * Adoption of the `Brain Imaging Data Structure standard <https://bids.neuroimaging.io/>`_ for data organization and the sample dataset available in data/ has been modified accordingly. (See :ref:`BIDS and BIDS App standards <cmpbids>` for more details)
-
 * MIALSRTK is going to Python with the creation of the ``pymialsrtk`` workflow library which extends the `Nipype dataflow library <https://nipype.readthedocs.io/en/latest/>`_ with the implementation of interfaces to all C++ MIALSRTK tools connected in a common workflow to perform super-resolution reconstruction of fetal brain MRI with data provenance and execution detail recordings. (See :ref:`API Documentation <api-doc>`)
-
 * Docker image encapsulating MIALSRTK is distributed as a BIDS App, a standard for containerized workflow that handles BIDS datasets with a set of predefined commandline input argument. (See :ref:`BIDS App Commadline Usage <cmdusage>` for more details)
-
 * Main documentation of MIALSRTK is rendered using readthedocs at https://mialsrtk.readthedocs.io/.
 
-
 New feature
 =============
 
@@ -77,7 +143,6 @@ New feature
 * ``pymialsrtk`` implements for convenience a Python wrapper that generates the Docker command line of the BIDS App for you,
 prints it out for reporting purposes, and then executes it without further action needed (See `pull request 47 <https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/pull/47>`_)
 
-
 Software development life cycle
 ================================
 
@@ -93,7 +158,6 @@ Software development life cycle
 
 * Use `coveragepy <https://coverage.readthedocs.io/en/coverage-5.2/>`_  in CircleCI during regression tests of the BIDS app and create code coverage reports published on our `Codacy project page <https://app.codacy.com/gh/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit/dashboard>`_.
 
-
 More...
 ========
 
diff --git a/documentation/conf.py b/documentation/conf.py
index 573303330..996ab8342 100644
--- a/documentation/conf.py
+++ b/documentation/conf.py
@@ -97,8 +97,9 @@ def visit_document(self, node):
 release = __version__
 
 rst_prolog = """
-.. |release| replace:: {}
-""".format(release)
+.. |pypirelease| replace:: {}
+.. |vrelease| replace:: {}
+""".format(f'pymialsrtk=={release}',f'v{release}')
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/documentation/images/html_report_1.png b/documentation/images/html_report_1.png
new file mode 100644
index 000000000..b87e89214
Binary files /dev/null and b/documentation/images/html_report_1.png differ
diff --git a/documentation/images/html_report_2.png b/documentation/images/html_report_2.png
new file mode 100644
index 000000000..28d0e76c0
Binary files /dev/null and b/documentation/images/html_report_2.png differ
diff --git a/documentation/images/html_report_3.png b/documentation/images/html_report_3.png
new file mode 100644
index 000000000..dbe1549e9
Binary files /dev/null and b/documentation/images/html_report_3.png differ
diff --git a/documentation/index.rst b/documentation/index.rst
index ccd172363..27d42b4f4 100644
--- a/documentation/index.rst
+++ b/documentation/index.rst
@@ -6,7 +6,7 @@ MIAL Super-Resolution Toolkit
   :width: 600
   :align: center
 
-**Latest released version:** |release|
+**Latest released version:** |vrelease|
 
 This neuroimaging processing pipeline software is developed by the Medical Image Analysis Laboratory (MIAL) at the University Hospital of Lausanne (CHUV) for use within the lab, as well as for open-source software distribution.
 
diff --git a/documentation/installation.rst b/documentation/installation.rst
index 157daf868..c4c342e74 100644
--- a/documentation/installation.rst
+++ b/documentation/installation.rst
@@ -83,17 +83,17 @@ Running Docker?
 
 * Open a terminal
 
-* Get the latest release (|release|) of the BIDS App:
+* Get the latest release (|vrelease|) of the BIDS App:
 
   .. parsed-literal::
 
-    $ docker pull sebastientourbier/mialsuperresolutiontoolkit:|release|
+    $ docker pull sebastientourbier/mialsuperresolutiontoolkit:|vrelease|
 
 * To display all docker images available::
 
   $ docker images
 
- You should see the docker image "mialsuperresolutiontoolkit" with tag "|release|" is now available.
+ You should see the docker image "mialsuperresolutiontoolkit" with tag "|vrelease|" is now available.
 
 * You are ready to use the Docker image of `MIALSRTK` from the terminal. See its `commandline usage <usage.html>`_.
 
@@ -102,11 +102,11 @@ Running Singularity?
 
 * Open a terminal
 
-* Get the latest release (|release|) of the BIDS App:
+* Get the latest release (|vrelease|) of the BIDS App:
 
   .. parsed-literal::
 
-    $ singularity pull library://tourbier/default/mialsuperresolutiontoolkit:|release|
+    $ singularity pull library://tourbier/default/mialsuperresolutiontoolkit:|vrelease|
 
 * You are ready to use the Singularity image of `MIALSRTK`. See its `commandline usage <usage.html>`_.
 
@@ -132,9 +132,9 @@ Once Python3 is installed, the ``mialsuperresolutiontoolkit_docker`` and ``mials
 
 * Installation with `pip`:
 
-  .. code-block:: console
+  .. parsed-literal::
 
-     $ pip install -e git+https://github.com/Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit#egg=pymialsrtk
+     $ pip install |pypirelease|
 
 * You are ready to use the ``mialsuperresolutiontoolkit_docker`` and ``mialsuperresolutiontoolkit_singularity`` wrappers. See their `commandline usages <wrapperusage>`_.
 
diff --git a/documentation/outputs.rst b/documentation/outputs.rst
index 64221bd88..3dce5761e 100644
--- a/documentation/outputs.rst
+++ b/documentation/outputs.rst
@@ -74,6 +74,45 @@ Anatomical derivatives
     - ``xfm/sub-<label>(_ses-<label>)_run-3_id-<label>_T2w_from-origin_to-SDI_mode-image_xfm.txt``
     - ...
 
+* The pipeline execution log file can be found in each subject's ``logs/`` subfolder as:
+
+    - ``logs/sub-01_rec-SR_id-1_log.txt``
+
+* The HTML processing report which provides in one place: pipeline/workflow configuration summary, Nipype workflow execution graph, links to the log and the profiling output report, plots for the quality check of the automatic reordering step based on the motion index, three orthogonal cuts of the reconstructed image, and computing environment summary. It is placed in each subject's ``report/`` subfolder:
+
+    - ``report/sub-<label>.html``
+
+    .. image:: images/html_report_1.png
+        :width: 600
+        :align: center
+    .. image:: images/html_report_2.png
+        :width: 600
+        :align: center
+    .. image:: images/html_report_3.png
+        :width: 600
+        :align: center
+
+* The PNG image generated by the stack auto-reordering node and used in the HTML processing report can be found in each subject's ``figures/`` subfolder as:
+
+    - ``figures/sub-01_rec-SR_id-1_desc-motion_stats.png``
+
+* The Nipype workflow execution graph used in the HTML processing report, which summarizes all processing nodes involves in the given processing pipeline, can be found in each subject's ``figures/`` folder as ``sub-01_rec-SR_id-1_desc-processing_graph.png``:
+
+    .. image:: images/nipype_wf_graph.png
+        :width: 600
+        :align: center
+
+.. topic:: Profiling report
+
+    The `--profiling` option flag enables `MIALSRTK` to monitor the computational resources used by each interface / node of the workflow.
+    It generates in each subject's ``logs/`` subfolder a ``sub-01_rec-SR_id-1_desc-profiling_log.html`` file which reports
+    run times, number of executed threads, and amount of used memory in the form of the following HTML gantt chart:
+
+    .. raw:: html
+
+        <iframe src="_static/run_stats.log.html" height="345px" width="100%"></iframe>
+
+
 Nipype Workflow Derivatives
 ==========================================
 
@@ -83,12 +122,6 @@ The execution of the Nipype workflow (pipeline) involves the creation of a numbe
     :width: 600
     :align: center
 
-To enhance transparency on how data is processed, outputs include a pipeline execution graph saved as ``srr_pipeline/graph.png`` which summarizes all processing nodes involves in the given processing pipeline:
-
-.. image:: images/nipype_wf_graph.png
-    :width: 600
-    :align: center
-
 Execution details (data provenance) of each interface (node) of a given pipeline are reported in ``srr_pipeline/<interface_name>/_report/report.rst``
 
 .. image:: images/nipype_node_report.png
diff --git a/documentation/usage.rst b/documentation/usage.rst
index c7e50af0b..26ac2ac92 100644
--- a/documentation/usage.rst
+++ b/documentation/usage.rst
@@ -126,7 +126,7 @@ For instance, the previous call to the ``mialsuperresolutiontoolkit_docker`` wra
     $ docker run -t --rm -u $(id -u):$(id -g) \\
             -v /home/localadmin/data/ds001:/bids_dir \\
             -v /media/localadmin/data/ds001/derivatives:/output_dir \\
-            sebastientourbier/mialsuperresolutiontoolkit:|release| \\
+            sebastientourbier/mialsuperresolutiontoolkit:|vrelease| \\
             /bids_dir /output_dir participant --participant_label 01 \\
             --param_file /bids_dir/code/participants_params.json \\
             (--openmp_nb_of_cores 4) \\
@@ -141,7 +141,7 @@ The previous call to the ``mialsuperresolutiontoolkit_singularity`` wrapper corr
     $ singularity run --containall \\
             --bind /home/localadmin/data/ds001:/bids_dir \\
             --bind /media/localadmin/data/ds001/derivatives:/output_dir \\
-            library://tourbier/default/mialsuperresolutiontoolkit:|release| \\
+            library://tourbier/default/mialsuperresolutiontoolkit:|vrelease| \\
             /bids_dir /output_dir participant --participant_label 01 \\
             --param_file /bids_dir/code/participants_params.json \\
             (--openmp_nb_of_cores 4) \\
diff --git a/notebooks/NiLearnPlotAnatSR.ipynb b/notebooks/NiLearnPlotAnatSR.ipynb
new file mode 100644
index 000000000..4d4e9473a
--- /dev/null
+++ b/notebooks/NiLearnPlotAnatSR.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "communist-richards",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pathlib\n",
+    "import nibabel as nib\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from nilearn.plotting import plot_anat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "multiple-conditions",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working directory: /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks\n"
+     ]
+    }
+   ],
+   "source": [
+    "working_dir = pathlib.Path(os.getcwd())\n",
+    "print(f'Working directory: {working_dir}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "expired-techno",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  > Load SR image /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/data/sub-01_rec-SR_id-1_T2w.nii.gz...\n",
+      "    Image properties: Zooms=(1.125, 1.125, 1.125)/ Shape=(320, 320, 73)/ FOV=[360.    360.     82.125]/ middle cut=[160, 160, 36]\n",
+      "  > Crop SR image at (100:220, 100:220, 0:-1)...\n",
+      "Save the PNG as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/sub-01_rec-SR_id-1_T2w.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 475.2x187.2 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "out_sr = pathlib.Path(working_dir / 'data' / 'sub-01_rec-SR_id-1_T2w.nii.gz')\n",
+    "out_sr_png = pathlib.Path(working_dir / 'sub-01_rec-SR_id-1_T2w.png')\n",
+    "\n",
+    "##################################\n",
+    "# Load the super-resolution image\n",
+    "##################################\n",
+    "print(f'  > Load SR image {out_sr}...')\n",
+    "img = nib.load(out_sr)\n",
+    "\n",
+    "# Get image properties\n",
+    "zooms = img.header.get_zooms()  # zooms are the size of the voxels\n",
+    "shape = img.shape\n",
+    "fov = np.array(zooms) * np.array(shape)\n",
+    "# Get middle cut\n",
+    "cut = [s // 2 for s in shape]\n",
+    "\n",
+    "print(f'    Image properties: Zooms={zooms}/ Shape={shape}/ FOV={fov}/ middle cut={cut}')\n",
+    "\n",
+    "####################################################\n",
+    "# Crop the image if the FOV exceeds a certain value\n",
+    "####################################################\n",
+    "def compute_axis_crop_indices(cut, fov, max_fov=120):\n",
+    "    '''Compute the cropping index in a dimension if the Field-Of-View exceeds a maximum value of 120mm by default.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    cut: int\n",
+    "        Middle slice index in a given dimension\n",
+    "        \n",
+    "    fov: float\n",
+    "        Slice Field-of-View (mm) in a given dimension\n",
+    "        \n",
+    "    max_fov: float\n",
+    "        Maximum Slice Field-of-View (mm) to which the image does not need to be cropped\n",
+    "        (120mm by default)\n",
+    "        \n",
+    "    Returns\n",
+    "    -------\n",
+    "    (crop_start_index, crop_end_index): (int, int)\n",
+    "        Starting and ending indices of the image crop along the given dimension\n",
+    "\n",
+    "    '''\n",
+    "    crop_start_index = cut - max_fov // 2 if fov > max_fov else 0\n",
+    "    crop_end_index = cut + max_fov // 2 if fov > max_fov else -1\n",
+    "    return (crop_start_index, crop_end_index)\n",
+    "   \n",
+    "max_fov = 120 # in mm\n",
+    "crop_start_x, crop_end_x = compute_axis_crop_indices(cut[0], fov[0], max_fov=max_fov)\n",
+    "crop_start_y, crop_end_y = compute_axis_crop_indices(cut[1], fov[1], max_fov=max_fov)\n",
+    "crop_start_z, crop_end_z = compute_axis_crop_indices(cut[2], fov[2], max_fov=max_fov)\n",
+    "\n",
+    "print(\n",
+    "    f'  > Crop SR image at '\n",
+    "    f'({crop_start_x}:{crop_end_x}, {crop_start_y}:{crop_end_y}, {crop_start_z}:{crop_end_z})...'\n",
+    ")\n",
+    "cropped_img = img.slicer[\n",
+    "    crop_start_x:crop_end_x,\n",
+    "    crop_start_y:crop_end_y,\n",
+    "    crop_start_z:crop_end_z\n",
+    "]\n",
+    "\n",
+    "###########################\n",
+    "# Create and save the plot\n",
+    "###########################\n",
+    "display = plot_anat(\n",
+    "    cut_coords= (6, 6, 6),\n",
+    "    anat_img=cropped_img,\n",
+    "    annotate=True,\n",
+    "    draw_cross=False,\n",
+    "    black_bg=True,\n",
+    "    dim='auto',\n",
+    "    display_mode='ortho',\n",
+    ")\n",
+    "print(f'Save the PNG as {out_sr_png}')\n",
+    "plt.savefig(\n",
+    "    out_sr_png,\n",
+    "    dpi=100,\n",
+    "    facecolor='k',\n",
+    "    edgecolor='k'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "chubby-order",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/StacksOrderSelection.ipynb b/notebooks/StacksOrderSelection.ipynb
index 6b9f6abf6..34287eaf0 100644
--- a/notebooks/StacksOrderSelection.ipynb
+++ b/notebooks/StacksOrderSelection.ipynb
@@ -1,5 +1,35 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1\n",
+      "Building wheels for collected packages: pymialsrtk\n",
+      "  Building wheel for pymialsrtk (setup.py): started\n",
+      "  Building wheel for pymialsrtk (setup.py): finished with status 'done'\n",
+      "  Created wheel for pymialsrtk: filename=pymialsrtk-2.0.2.dev0-py3-none-any.whl size=174085433 sha256=93ab64303a0db9741479e029747d49a7df32fef25967698698a4ab87028eed27\n",
+      "  Stored in directory: /Users/sebastientourbier/Library/Caches/pip/wheels/7e/e7/e7/f2999683d01f3a046157fafe8d3e57f99e2d0367c0e2d45e88\n",
+      "Successfully built pymialsrtk\n",
+      "Installing collected packages: pymialsrtk\n",
+      "  Attempting uninstall: pymialsrtk\n",
+      "    Found existing installation: pymialsrtk 2.0.2.dev0\n",
+      "    Uninstalling pymialsrtk-2.0.2.dev0:\n",
+      "      Successfully uninstalled pymialsrtk-2.0.2.dev0\n",
+      "Successfully installed pymialsrtk-2.0.2.dev0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "pip install ../"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -21,29 +51,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BIDS Layout: .../fetaldata | Subjects: 9 | Sessions: 10 | Runs: 57\n"
-     ]
-    }
-   ],
-   "source": [
-    "bids_dir = os.path.join('/fetaldata')\n",
-    "mask_dir = os.path.join('/fetaldata','derivatives','manual_masks')\n",
-    "\n",
-    "subject = '01'\n",
-    "\n",
-    "layout = BIDSLayout(bids_dir,derivatives=mask_dir)\n",
-    "print(layout)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [],
    "source": [
     "from traits.api import *\n",
@@ -54,206 +61,175 @@
     "from nipype.interfaces.base import traits, isdefined, CommandLine, CommandLineInputSpec,\\\n",
     "    TraitedSpec, File, InputMultiPath, OutputMultiPath, BaseInterface, BaseInterfaceInputSpec\n",
     "\n",
-    "from nipype.interfaces.mixins import reporting\n",
-    "\n"
+    "from nipype.interfaces.mixins import reporting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "import nibabel as nib\n",
     "import numpy as np\n",
-    "import skimage.measure\n"
+    "import skimage.measure"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/miniconda3/envs/pymialsrtk-env/lib/python3.7/site-packages/bids/layout/models.py:152: FutureWarning: The 'extension' entity currently excludes the leading dot ('.'). As of version 0.14.0, it will include the leading dot. To suppress this warning and include the leading dot, use `bids.config.set_option('extension_initial_dot', True)`.\n",
+      "  FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BIDS Layout: ...lsuperresolutiontoolkit-1/data | Subjects: 1 | Sessions: 0 | Runs: 6\n"
+     ]
+    }
+   ],
    "source": [
-    "class StacksOrderingInputSpec(BaseInterfaceInputSpec):\n",
-    "    \"\"\"Class used to represent inputs of the StacksOrdering interface.\n",
-    "\n",
-    "    Attributes\n",
-    "    -----------\n",
-    "    input_masks <list<string>>\n",
-    "        Input brain masks on which motion is computed.\n",
-    "\n",
-    "    See also\n",
-    "    --------------\n",
-    "    pymialsrtk.interfaces.preprocess.StacksOrdering\n",
+    "bids_dir = os.path.join('/Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/data')\n",
+    "mask_dir = os.path.join(bids_dir, 'derivatives', 'manual_masks')\n",
     "\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    input_masks = InputMultiPath(File(desc='Input masks',mandatory=True))\n",
-    "    \n",
-    "    \n",
-    "class StacksOrderingOutputSpec(TraitedSpec):\n",
-    "    \"\"\"Class used to represent outputs of the StacksOrdering interface.\n",
-    "\n",
-    "    Attributes\n",
-    "    -----------\n",
-    "    stacks_order <string>\n",
-    "        Order of images' run-id to be used for reconstruction\n",
-    "\n",
-    "    See also\n",
-    "    --------------\n",
-    "    pymialsrtk.interfaces.preprocess.StacksOrdering\n",
-    "\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    stacks_order = traits.List(desc = 'Order of stacks')\n",
-    "\n",
-    "    \n",
-    "class StacksOrdering(BaseInterface):\n",
-    "    \"\"\"Runs the automatic ordering of stacks.\n",
-    "\n",
-    "    This module is based on a centroid tracking of the the central of the brain mask.\n",
-    "\n",
-    "    Examples\n",
-    "    --------\n",
-    "    >>> from pymialsrtk.interfaces.preprocess import StacksOrdering\n",
-    "    >>> stacksOrdering = StacksOrdering()\n",
-    "    >>> stacksOrdering.inputs.input_images = ['my_image_run-1.nii.gz', 'my_image_run-3.nii.gz', 'my_image_run-2.nii.gz']\n",
-    "    >>> stacksOrdering.run() # doctest: +SKIP\n",
-    "\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    input_spec = StacksOrderingInputSpec\n",
-    "    output_spec = StacksOrderingOutputSpec\n",
-    "    \n",
-    "    m_stack_order = []\n",
-    "    \n",
-    "    def _run_interface(self, runtime): \n",
-    "        try:\n",
-    "            self.m_stack_order = self._compute_stack_order(self.inputs.input_masks)\n",
-    "        except Exception as e:\n",
-    "            print('Failed')\n",
-    "            \n",
-    "            print(e)\n",
-    "        return runtime\n",
-    "\n",
-    "    def _list_outputs(self):\n",
-    "        outputs = self._outputs().get()\n",
-    "        outputs['stacks_order'] = self.m_stack_order\n",
-    "        return outputs\n",
-    "    \n",
-    "    def _compute_motion_index(self, in_file):\n",
-    "        \"\"\"Function to compute the motion index.\n",
-    "\n",
-    "        The motion index is computed from the inter-slice displacement of the centroid of the brain mask.  \n",
-    "        \"\"\"\n",
-    "        central_third = True\n",
-    "\n",
-    "        img = nib.load(in_file)\n",
-    "\n",
-    "        voxelspacing = img.header['pixdim'][2]\n",
-    "        data = img.get_fdata()\n",
-    "\n",
-    "        z = np.where(data)[2]        \n",
-    "        data=data[...,int(min(z)):int(max(z)+1)]\n",
-    "\n",
-    "        if central_third:\n",
-    "            num_z=data.shape[2]\n",
-    "            center_z=int(num_z/2.)\n",
-    "            \n",
-    "            data=data[...,int(center_z-num_z/6.):int(center_z+num_z/6.)]\n",
-    "\n",
-    "        centroid_coord=np.zeros((data.shape[2],2))\n",
-    "\n",
-    "        for i in range(data.shape[2]):\n",
-    "            moments=skimage.measure.moments(data[...,i])\n",
-    "            centroid_coord[i,:]=[moments[0,1]/moments[0,0],moments[1,0]/moments[0,0]]\n",
-    "\n",
-    "        centroid_coord = centroid_coord[~np.isnan(centroid_coord)]\n",
-    "        centroid_coord = np.reshape(centroid_coord,(int(centroid_coord.shape[0]/2),2))\n",
-    "\n",
-    "        nSlices = data.shape[2]\n",
-    "        score = (np.var(centroid_coord[:,0])+np.var(centroid_coord[:,1])) / nSlices\n",
-    "\n",
-    "        return score\n",
-    "\n",
-    "\n",
-    "    def _compute_stack_order(self, in_files):\n",
-    "        \"\"\"Function to compute the stacks order.\n",
-    "\n",
-    "        The motion index is computed for each mask. Stacks are ordered according to their motion index. \n",
-    "        \"\"\"\n",
-    "        motion_ind = []\n",
-    "\n",
-    "        vp_defined = not -1 in [ f.find('vp') for f in in_files]\n",
-    "        \n",
-    "        print('vp_defined', vp_defined)\n",
-    "        if vp_defined:\n",
-    "            orientations_ = []\n",
-    "            for f in in_files:\n",
-    "                orientations_.append((f.split('_vp-')[1]).split('_')[0])\n",
-    "        \n",
-    "\n",
-    "        for f in in_files:\n",
-    "            motion_ind.append(self._compute_motion_index(f))\n",
-    "\n",
-    "            \n",
-    "        if vp_defined:\n",
-    "            motion_ind_sorted, images_ordered, orientations_ordered = (list(t) for t in zip(*sorted(zip(motion_ind, in_files, orientations_))))\n",
-    "        else:\n",
-    "            motion_ind_sorted, images_ordered = (list(t) for t in zip(*sorted(zip(motion_ind, in_files))))\n",
-    "        \n",
-    "        run_order = [ int(f.split('run-')[1].split('_')[0]) for f in images_ordered]\n",
-    "        \n",
-    "        if vp_defined:\n",
-    "                \n",
-    "            first_ax = orientations_ordered.index('ax')\n",
-    "            first_sag = orientations_ordered.index('sag')\n",
-    "            first_cor = orientations_ordered.index('cor')\n",
-    "            firsts = [first_ax, first_cor, first_sag]\n",
-    "            \n",
-    "            run_tmp = run_order\n",
-    "            run_order = []\n",
-    "            ind_ = firsts.index(min(firsts))\n",
-    "            run_order.append(int(images_ordered[firsts[ind_]].split('run-')[1].split('_')[0]))\n",
-    "            \n",
-    "            \n",
-    "            firsts.pop(ind_)\n",
-    "            ind_ = firsts.index(min(firsts))\n",
-    "            run_order.append(int(images_ordered[firsts[ind_]].split('run-')[1].split('_')[0]))\n",
-    "            \n",
-    "            \n",
-    "            firsts.pop(ind_)\n",
-    "            ind_ = firsts.index(min(firsts))\n",
-    "            \n",
-    "            run_order.append(int(images_ordered[firsts[ind_]].split('run-')[1].split('_')[0]))\n",
-    "            \n",
-    "            others = [ e for e in run_tmp if e not in run_order]   \n",
-    "            run_order += others\n",
+    "subject = '01'\n",
     "\n",
-    "        return run_order\n",
-    "        "
+    "layout = BIDSLayout(bids_dir, derivatives=mask_dir)\n",
+    "print(layout)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "vp_defined True\n"
+      "WARNING:tensorflow:From /Applications/miniconda3/envs/pymialsrtk-env/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pymialsrtk.interfaces.preprocess import StacksOrdering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Info: Number of NaNs = 0\n",
+      "  Info: Number of NaNs = 0\n",
+      "  Info: Number of NaNs = 0\n",
+      "  Info: Number of NaNs = 0\n",
+      "  Info: Number of NaNs = 0\n",
+      "  Info: Number of NaNs = 0\n",
+      "\t>> Create report image...\n",
+      "\t\t\t - Format data...\n",
+      "\t\t\t - Create DataFrame...\n",
+      "     Scan  Slice  Motion Index  Proportion of NaNs (%)    X (mm)    Y (mm)  \\\n",
+      "5   run-2  run-2      0.023707                     0.0  0.440528  0.248819   \n",
+      "6   run-2  run-2      0.023707                     0.0  0.143335 -0.194660   \n",
+      "7   run-2  run-2      0.023707                     0.0 -0.058334  0.358721   \n",
+      "8   run-2  run-2      0.023707                     0.0  0.445515 -0.582403   \n",
+      "9   run-2  run-2      0.023707                     0.0 -0.971044  0.169523   \n",
+      "24  run-5  run-5      0.034545                     0.0 -0.115317 -0.936588   \n",
+      "25  run-5  run-5      0.034545                     0.0 -0.691212  0.427981   \n",
+      "26  run-5  run-5      0.034545                     0.0  0.151605  0.099944   \n",
+      "27  run-5  run-5      0.034545                     0.0 -0.366834  0.162614   \n",
+      "28  run-5  run-5      0.034545                     0.0  1.021758  0.246050   \n",
+      "10  run-3  run-3      0.034858                     0.0  0.745116 -1.506861   \n",
+      "11  run-3  run-3      0.034858                     0.0  0.145297 -0.771043   \n",
+      "12  run-3  run-3      0.034858                     0.0 -0.257684  0.632681   \n",
+      "13  run-3  run-3      0.034858                     0.0 -0.119751  0.696198   \n",
+      "14  run-3  run-3      0.034858                     0.0  0.070042 -0.040388   \n",
+      "15  run-3  run-3      0.034858                     0.0  0.042174  0.907495   \n",
+      "16  run-3  run-3      0.034858                     0.0 -0.625193  0.081918   \n",
+      "17  run-4  run-4      0.062694                     0.0 -0.241700  1.815984   \n",
+      "18  run-4  run-4      0.062694                     0.0 -0.813570 -1.857281   \n",
+      "19  run-4  run-4      0.062694                     0.0 -0.753517  0.271336   \n",
+      "20  run-4  run-4      0.062694                     0.0  0.264512 -0.268836   \n",
+      "21  run-4  run-4      0.062694                     0.0  0.146040  0.500229   \n",
+      "22  run-4  run-4      0.062694                     0.0  1.212980 -0.194121   \n",
+      "23  run-4  run-4      0.062694                     0.0  0.185255 -0.267311   \n",
+      "29  run-6  run-6      0.087105                     0.0 -0.125714 -1.131355   \n",
+      "30  run-6  run-6      0.087105                     0.0 -0.231452  0.275648   \n",
+      "31  run-6  run-6      0.087105                     0.0 -0.944095  0.118737   \n",
+      "32  run-6  run-6      0.087105                     0.0 -0.398724 -0.507579   \n",
+      "33  run-6  run-6      0.087105                     0.0  1.699985  1.244549   \n",
+      "0   run-1  run-1      0.271740                     0.0  1.996426 -0.045610   \n",
+      "1   run-1  run-1      0.271740                     0.0 -1.490401  0.423500   \n",
+      "2   run-1  run-1      0.271740                     0.0  1.273363 -0.629354   \n",
+      "3   run-1  run-1      0.271740                     0.0 -3.254713  0.903943   \n",
+      "4   run-1  run-1      0.271740                     0.0  1.475325 -0.652480   \n",
+      "\n",
+      "    Displacement Magnitude (mm)  \n",
+      "5                      0.505941  \n",
+      "6                      0.241739  \n",
+      "7                      0.363433  \n",
+      "8                      0.733265  \n",
+      "9                      0.985730  \n",
+      "24                     0.943661  \n",
+      "25                     0.812983  \n",
+      "26                     0.181584  \n",
+      "27                     0.401261  \n",
+      "28                     1.050967  \n",
+      "10                     1.681019  \n",
+      "11                     0.784613  \n",
+      "12                     0.683144  \n",
+      "13                     0.706422  \n",
+      "14                     0.080852  \n",
+      "15                     0.908475  \n",
+      "16                     0.630537  \n",
+      "17                     1.831998  \n",
+      "18                     2.027656  \n",
+      "19                     0.800882  \n",
+      "20                     0.377147  \n",
+      "21                     0.521111  \n",
+      "22                     1.228415  \n",
+      "23                     0.325230  \n",
+      "29                     1.138318  \n",
+      "30                     0.359933  \n",
+      "31                     0.951532  \n",
+      "32                     0.645459  \n",
+      "33                     2.106858  \n",
+      "0                      1.996947  \n",
+      "1                      1.549402  \n",
+      "2                      1.420401  \n",
+      "3                      3.377909  \n",
+      "4                      1.613169  \n",
+      "\t\t\t - Create Boxplot...\n",
+      "\t\t\t - Save report image 0 as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC_0.png...\n",
+      "\t\t\t - Save report image 1 as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC_1.png...\n",
+      "\t\t\t - Save report image 2 as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC_2.png...\n",
+      "\t\t\t - Save report image 3 as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC_3.png...\n",
+      "\t\t\t - Save final report image as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC.png...\n",
+      "\t\t\t - List output report image as /Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/motion_index_QC.png...\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[2, 3, 6, 5, 4, 1]"
+       "[2, 5, 3, 4, 6, 1]"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,12 +237,14 @@
    "source": [
     "stacksOrdering = StacksOrdering()\n",
     "\n",
-    "stacksOrdering.inputs.input_masks = ['/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-1_T2w_vp-sag_desc-brain_mask.nii.gz',\n",
-    "                        '/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-2_T2w_vp-cor_desc-brain_mask.nii.gz',\n",
-    "                         '/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-3_T2w_vp-ax_desc-brain_mask.nii.gz',\n",
-    "                        '/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-4_T2w_vp-ax_desc-brain_mask.nii.gz',\n",
-    "                         '/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-5_T2w_vp-cor_desc-brain_mask.nii.gz',\n",
-    "                        '/fetaldata/derivatives/manual_masks/sub-01/anat/sub-01_run-6_T2w_vp-sag_desc-brain_mask.nii.gz']\n",
+    "stacksOrdering.inputs.input_masks = [\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-1_T2w_desc-brain_mask.nii.gz',\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-2_T2w_desc-brain_mask.nii.gz',\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-3_T2w_desc-brain_mask.nii.gz',\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-4_T2w_desc-brain_mask.nii.gz',\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-5_T2w_desc-brain_mask.nii.gz',\n",
+    "    mask_dir + '/sub-01/anat/sub-01_run-6_T2w_desc-brain_mask.nii.gz'\n",
+    "]\n",
     "\n",
     "aa = stacksOrdering.run() # doctest: +SKIP\n",
     "aa.outputs.stacks_order"
@@ -325,7 +303,10 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "%%bash\n",
+    "pip install matplotlib==3.4"
+   ]
   },
   {
    "cell_type": "code",
@@ -337,7 +318,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -351,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/SubjectReport.ipynb b/notebooks/SubjectReport.ipynb
new file mode 100644
index 000000000..9b3374815
--- /dev/null
+++ b/notebooks/SubjectReport.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "systematic-ultimate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "pip install ../"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "boxed-profile",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import pkg_resources\n",
+    "\n",
+    "from nipype.pipeline import Node\n",
+    "\n",
+    "from jinja2 import Environment, FileSystemLoader\n",
+    "\n",
+    "from pymialsrtk.info import __version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continued-whale",
+   "metadata": {},
+   "source": [
+    "## Test the interface implemented in `pymialsrtk.interfaces.report`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "chief-business",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "210721-14:34:52,311 nipype.workflow INFO:\n",
+      "\t [Node] Setting-up \"subject_report\" in \"/Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/subject_report\".\n",
+      "210721-14:34:52,316 nipype.workflow INFO:\n",
+      "\t [Node] Running \"subject_report\" (\"pymialsrtk.interfaces.report.MialsrtkSubjectReportInterface\")\n",
+      "DEBUG: Report content=<!DOCTYPE html>\n",
+      "<html lang=\"en\">\n",
+      "<title>Super-Resolution Report of Subject 01</title>\n",
+      "<meta name=\"description\" content=\"Super-Resolution Report of Subject 01.\">\n",
+      "<meta name=\"author\" content=\"MIAL Super-Resolution ToolKit 2.0.2-dev\">\n",
+      "<meta charset=\"UTF-8\">\n",
+      "<meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n",
+      "<link rel=\"stylesheet\" href=\"https://www.w3schools.com/w3css/4/w3.css\">\n",
+      "<link rel=\"stylesheet\" href=\"https://www.w3schools.com/lib/w3-theme-black.css\">\n",
+      "<link rel=\"stylesheet\" href=\"https://fonts.googleapis.com/css?family=Roboto\">\n",
+      "<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\">\n",
+      "<style>\n",
+      "html,body,h1,h2,h3,h4,h5,h6 {font-family: \"Roboto\", sans-serif;}\n",
+      ".w3-sidebar {\n",
+      "  z-index: 3;\n",
+      "  width: 250px;\n",
+      "  top: 43px;\n",
+      "  bottom: 0;\n",
+      "  height: inherit;\n",
+      "}\n",
+      "</style>\n",
+      "<body>\n",
+      "\n",
+      "<!-- Main content: shift it to the right by 250 pixels when the sidebar is visible -->\n",
+      "<div class=\"w3-main\" style=\"margin-left:150px\">\n",
+      "  <h1 class=\"w3-text-teal\">Super-Resolution Report of Subject 01</h1>\n",
+      "\n",
+      "  <div class=\"w3-row w3-padding-16\">\n",
+      "    <div class=\"w3-container\">\n",
+      "      <p>\n",
+      "        <ul>\n",
+      "          <li>Number of scans: 3</li>\n",
+      "          <li>In-plane resolution: 1.125mm x 1.125mm</li>\n",
+      "          <li>Slice thickness: 1.125mm</li>\n",
+      "        </ul>\n",
+      "      </p>\n",
+      "    </div>\n",
+      "  </div>\n",
+      "\n",
+      "  <div class=\"w3-container\">\n",
+      "    <h1 class=\"w3-text-teal\">Workflow</h1>\n",
+      "    <p class=\"w3-padding-large w3-padding-8 w3-center\">\n",
+      "      <img src= width=\"800\">\n",
+      "    </p>\n",
+      "  </div>\n",
+      "\n",
+      "  <div class=\"w3-row w3-padding-16\">\n",
+      "    <div class=\"w3-third w3-container\">\n",
+      "      <h1 class=\"w3-text-teal\">Motion</h1>\n",
+      "      <p>\n",
+      "        <ul>\n",
+      "          <li>Input run index order : [6, 3, 1]</li>\n",
+      "        </ul>\n",
+      "      </p>\n",
+      "    </div>\n",
+      "    <div class=\"w3-twothird w3-container\">\n",
+      "      <p class=\"w3-padding-large w3-padding-32 w3-center\">\n",
+      "          <img src=\"/Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/data/sub-01_rec-SR_id-1_desc-motion_stats.png\" width=\"800\">\n",
+      "      </p>\n",
+      "    </div>\n",
+      "  </div>\n",
+      "\n",
+      "  <div class=\"w3-row\">\n",
+      "    <div class=\"w3-third w3-container\">\n",
+      "      <h1 class=\"w3-text-teal\">Super-Resolution</h1>\n",
+      "      <p>\n",
+      "        <ul>\n",
+      "          <li>Algorithm: Total-Variation</li>\n",
+      "          <li>Regularization weight (Lambda): 0.75</li>\n",
+      "          <li>Optimization time step: 0.01</li>\n",
+      "          <li>Number of primal/dual iterations: 10</li>\n",
+      "        </ul>\n",
+      "\n",
+      "      .</p>\n",
+      "    </div>\n",
+      "    <div class=\"w3-twothird w3-container\">\n",
+      "      <p class=\"w3-padding-large w3-padding-32 w3-center\">\n",
+      "          <img src=\"/Users/sebastientourbier/Softwares/forks/mialsuperresolutiontoolkit-1/notebooks/data/sub-01_rec-SR_id-1_T2w.png\" width=\"800\">\n",
+      "      </p>\n",
+      "    </div>\n",
+      "  </div>\n",
+      "\n",
+      "  <footer id=\"myFooter\">\n",
+      "    <div class=\"w3-container w3-theme-cyan w3-padding-8\">\n",
+      "      <h4>Generated by MIALSRTK version 2.0.2-dev</h4>\n",
+      "    </div>\n",
+      "\n",
+      "    <div class=\"w3-container w3-theme-l1\">\n",
+      "      <p>Copyright &copy;\n",
+      "        2016-2021 Medical Image Analysis Laboratory,\n",
+      "        University Hospital Center and University of Lausanne (UNIL-CHUV),\n",
+      "        Switzerland, and contributors</p>\n",
+      "      <p>Powered by <a href=\"https://www.w3schools.com/w3css/default.asp\" target=\"_blank\">w3.css</a></p>\n",
+      "    </div>\n",
+      "  </footer>\n",
+      "\n",
+      "<!-- END MAIN -->\n",
+      "</div>\n",
+      "\n",
+      "</body>\n",
+      "</html>\n",
+      "DEBUG: Save as sub-01.html...\n",
+      "210721-14:34:52,370 nipype.workflow INFO:\n",
+      "\t [Node] Finished \"subject_report\".\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pymialsrtk.interfaces.report import MialsrtkSubjectReportInterface\n",
+    "\n",
+    "subj_report = Node(\n",
+    "    interface=MialsrtkSubjectReportInterface(),\n",
+    "    name='subject_report',\n",
+    "    base_dir='.'\n",
+    ")\n",
+    "subj_report.inputs.subject = '01'\n",
+    "subj_report.inputs.sr_nii_image = os.path.abspath('data/sub-01_rec-SR_id-1_T2w.nii.gz')\n",
+    "subj_report.inputs.sr_json_metadata = os.path.abspath('data/sub-01_rec-SR_id-1_T2w.json')\n",
+    "subj_report.inputs.sr_png_image = os.path.abspath('data/sub-01_rec-SR_id-1_T2w.png')\n",
+    "subj_report.inputs.motion_report_image = os.path.abspath('data/sub-01_rec-SR_id-1_desc-motion_stats.png')\n",
+    "subj_report.inputs.workflow_image = os.path.abspath('data/graph.png')\n",
+    "out = subj_report.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dense-huntington",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data/graph.png b/notebooks/data/graph.png
new file mode 100644
index 000000000..664f151d3
Binary files /dev/null and b/notebooks/data/graph.png differ
diff --git a/notebooks/data/sub-01_rec-SR_id-1_T2w.json b/notebooks/data/sub-01_rec-SR_id-1_T2w.json
new file mode 100644
index 000000000..c05257467
--- /dev/null
+++ b/notebooks/data/sub-01_rec-SR_id-1_T2w.json
@@ -0,0 +1,15 @@
+{
+    "Description": "Isotropic high-resolution image reconstructed using the Total-Variation Super-Resolution algorithm provided by MIALSRTK",
+    "Input sources run order": [
+        6,
+        3,
+        1
+    ],
+    "CustomMetaData": {
+        "Number of scans used": "3",
+        "Masks used": "Automatic",
+        "TV regularization weight lambda": 0.75,
+        "Optimization time step": 0.01,
+        "Primal/dual loops": 10
+    }
+}
\ No newline at end of file
diff --git a/notebooks/data/sub-01_rec-SR_id-1_T2w.nii.gz b/notebooks/data/sub-01_rec-SR_id-1_T2w.nii.gz
new file mode 100644
index 000000000..edc5c92dd
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new file mode 100644
index 000000000..1ac5780ee
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diff --git a/notebooks/data/sub-01_rec-SR_id-1_desc-motion_stats.png b/notebooks/data/sub-01_rec-SR_id-1_desc-motion_stats.png
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diff --git a/pymialsrtk/bids/__init__.py b/pymialsrtk/bids/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pymialsrtk/bids/utils.py b/pymialsrtk/bids/utils.py
new file mode 100644
index 000000000..a6a3cb4ec
--- /dev/null
+++ b/pymialsrtk/bids/utils.py
@@ -0,0 +1,105 @@
+# Copyright © 2016-2021 Medical Image Analysis Laboratory,
+# University Hospital Center and University of Lausanne (UNIL-CHUV),
+# Switzerland, and contributors
+#
+#  This software is distributed under the open-source license Modified BSD.
+
+"""This modules provides CMTK Utility functions to handle BIDS datasets."""
+
+import os
+import json
+
+from nipype import __version__ as nipype_version
+from pymialsrtk.info import __version__, __url__, DOCKER_HUB
+
+
+def write_bids_derivative_description(bids_dir, deriv_dir, pipeline_name):
+    """Write a dataset_description.json in each type of PyMIALSRTK derivatives.
+
+    Parameters
+    ----------
+    bids_dir : string
+        BIDS root directory
+    deriv_dir : string
+        Output/derivatives directory
+    pipeline_name : string
+        Type of derivatives (`['pymialsrtk', 'nipype']`)
+
+    """
+    bids_dir = os.path.abspath(bids_dir)
+    deriv_dir = os.path.abspath(deriv_dir)
+
+    if pipeline_name == "pymialsrtk":
+        desc = {
+            "Name": "PyMIALSRTK Outputs",
+            "BIDSVersion": "1.4.0",
+            "DatasetType": "derivatives",
+            "GeneratedBy": {
+                "Name": f'{pipeline_name}',
+                "Version": __version__,
+                "Container": {
+                    "Type": "docker",
+                    "Tag": "{}:{}".format(DOCKER_HUB, __version__),
+                },
+                "CodeURL": __url__,
+            },
+            "PipelineDescription": {"Name": "PyMIALSRTK Outputs ({})".format(__version__)},
+            "HowToAcknowledge": "Please cite ... ",
+        }
+    elif pipeline_name == "nipype":
+        desc = {
+            "Name": "Nipype Outputs of PyMIALSRTK ({})".format(__version__),
+            "BIDSVersion": "1.4.0",
+            "DatasetType": "derivatives",
+            "GeneratedBy": {
+                "Name": pipeline_name,
+                "Version": nipype_version,
+                "Container": {
+                    "Type": "docker",
+                    "Tag": "{}:{}".format(DOCKER_HUB, __version__),
+                },
+                "CodeURL": __url__,
+            },
+            "PipelineDescription": {
+                "Name": "Nipype Outputs of PyMIALSRTK ({})".format(__version__),
+            },
+            "HowToAcknowledge": "Please cite ... ",
+        }
+
+    # Keys deriving from source dataset
+    orig_desc = {}
+    fname = os.path.join(bids_dir, "dataset_description.json")
+    if os.access(fname, os.R_OK):
+        with open(fname, "r") as fobj:
+            orig_desc = json.load(fobj)
+
+    if "DatasetDOI" in orig_desc:
+        desc["SourceDatasets"]: [
+            {
+                "DOI": orig_desc["DatasetDOI"],
+                "URL": "https://doi.org/{}".format(orig_desc["DatasetDOI"]),
+                "Version": "TODO: To be updated",
+            }
+        ]
+    else:
+        desc["SourceDatasets"]: [
+            {
+                "DOI": "TODO: To be updated",
+                "URL": "TODO: To be updated",
+                "Version": "TODO: To be updated",
+            }
+        ]
+
+    desc[
+        "License"
+    ] = "TODO: To be updated (See https://creativecommons.org/about/cclicenses/)"
+
+    # Save the dataset_description.json file
+    output_filename = os.path.join(
+        deriv_dir,
+        f'{desc["GeneratedBy"]["Name"]}-{desc["GeneratedBy"]["Version"]}',
+        "dataset_description.json"
+    )
+    print(f'\tSave {desc["GeneratedBy"]["Name"]} bids tool description to {output_filename}...')
+    with open(output_filename, 'w+') as fobj:
+        json.dump(desc, fobj, indent=4)
diff --git a/pymialsrtk/data/report/templates/template.html b/pymialsrtk/data/report/templates/template.html
new file mode 100644
index 000000000..9454245d6
--- /dev/null
+++ b/pymialsrtk/data/report/templates/template.html
@@ -0,0 +1,212 @@
+<!DOCTYPE html>
+<html lang="en">
+<title>Processing Report - Subject {{subject}}</title>
+<meta name="description" content="MIALSRTK Super-Resolution Processing Report of Subject {{subject}}.">
+<meta name="author" content="MIAL Super-Resolution ToolKit {{version}}">
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
+<link rel="stylesheet" href="https://www.w3schools.com/lib/w3-theme-black.css">
+<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto">
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
+<style>
+html,body,h1,h2,h3,h4,h5,h6 {font-family: "Roboto", sans-serif;}
+.w3-sidebar {
+  z-index: 3;
+  width: 250px;
+  top: 43px;
+  bottom: 0;
+  height: inherit;
+}
+img {
+  max-width: 100%;
+  height: auto;
+}
+
+</style>
+<body>
+
+
+<!-- Main content: shift it to the right by 100 pixels -->
+<div class="w3-main" style="margin-left:100px">
+  <h1 class="w3-text-teal">Super-Resolution Reconstruction Report</h1>
+
+  <div class="w3-row w3-padding-16">
+    <div class="w3-container">
+      <p>
+        <ul>
+          <li><span class="w3-text-orange">Subject:</span> {{subject}}</li>
+          {% if session is defined and session is not none %}
+          <li><span class="w3-text-orange">Session:</span> {{session}}</li>
+          {% endif %}
+          <li><span class="w3-text-orange">Reconstruction ID:</span> {{sr_id}}</li>
+        </ul>
+      </p>
+    </div>
+  </div>
+
+  <div class="w3-row w3-padding-16">
+    <h1 class="w3-text-teal">Configuration</h1>
+    <div class="w3-third w3-container">
+      <p>
+        <ul>
+          <li><span class="w3-text-orange">Number of scans:</span> {{sr_json_metadata["CustomMetaData"]["Number of scans used"]}}</li>
+          <li><span class="w3-text-orange">Original scan run index order:</span> {{stacks}}</li>
+          {% if stacks_ordering=="on" %}
+          <li><span class="w3-text-orange">Motion-based scan auto-ordering:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">Motion-based scan auto-ordering:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+          {% if use_auto_masks=="on" %}
+          <li><span class="w3-text-orange">Automatic brain extraction:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">Automatic brain extraction:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+          {% if custom_masks_dir is defined and custom_masks_dir is not none %}
+          <li><span class="w3-text-orange">Custom brain mask directory:</span> <span class="w3-light-grey w3-text-dark-grey">derivatives/{{custom_masks_dir}}</span></li>
+          {% endif %}
+        </ul>
+      </p>
+    </div>
+    <div class="w3-twothird w3-container">
+      <p>
+        <ul>
+          {% if nlm_denoising=="on" %}
+          <li><span class="w3-text-orange">NLM denoising:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">NLM denoising:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+          {% if svr=="on" %}
+          <li><span class="w3-text-orange">Slice-to-volume registration:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">Slice-to-volume registration:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+          {% if do_refine_hr_mask=="on" %}
+          <li><span class="w3-text-orange">SR brain mask refinement:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">SR brain mask refinement:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+          {% if resources_stats is defined and resources_stats is not none %}
+          <li><span class="w3-text-orange">Computational resources profiling:</span> <span class="w3-text-green">&#x2713;</span></li>
+          {% else %}
+          <li><span class="w3-text-orange">Computational resources profiling:</span> <span class="w3-text-red">&#x2717;</span></li>
+          {% endif %}
+        </ul>
+      </p>
+    </div>
+  </div>
+
+  <div class="w3-row w3-padding-16">
+    <h1 class="w3-text-teal">Workflow</h1>
+    <div class="w3-container">
+      <p class="w3-padding-large w3-padding-8 w3-center">
+        <img src="{{workflow_graph}}">
+      </p>
+    </div>
+  </div>
+
+  <div class="w3-row w3-padding-16">
+    <h1 class="w3-text-teal">Execution</h1>
+    <div class="w3-container">
+      <p>
+        <ul>
+          <li><span class="w3-text-orange">Starting processing date / time:</span> {{processing_datetime}}</li>
+          <li><span class="w3-text-orange">Elapsed time:</span> {{run_time}}</li>
+          <li><span class="w3-text-orange">Log file:</span> <a href="{{log}}"><span class="w3-light-grey w3-text-dark-grey">{{log}}</span></a></li>
+           {% if resources_stats is defined and resources_stats is not none %}
+           <li><span class="w3-text-orange">Computational resources report:</span> <a href="{{resources_stats}}"><span class="w3-light-grey w3-text-dark-grey">{{resources_stats}}</span></a></li>
+           {% endif %}
+        </ul>
+      </p>
+    </div>
+  </div>
+
+  {% if stacks_ordering=="on" %}
+  <div class="w3-row w3-padding-16">
+    <h1 class="w3-text-teal">Motion and stack order</h1>
+    <div class="w3-container">
+      <p class="w3-padding-large w3-padding-32 w3-center">
+          <img src="{{motion_report_image}}">
+      </p>
+    </div>
+  </div>
+  {% endif %}
+
+  <div class="w3-row">
+    <h1 class="w3-text-teal">Super-Resolution</h1>
+    <div class="w3-half w3-container">
+      <p>
+        <ul>
+          <li><span class="w3-text-orange">Input scan run index order:</span> {{sr_json_metadata["Input sources run order"]}}</li>
+          <li><span class="w3-text-orange">Output resolution:</span> {{sr_resolution}}</li>
+          <li><span class="w3-text-orange">Algorithm:</span> Total-Variation</li>
+        </ul>
+      </p>
+    </div>
+    <div class="w3-half w3-container">
+      <p>
+        <h1 class="w3-text-teal"> </h1>
+        <ul>
+          <li><span class="w3-text-orange">Regularization weight (Lambda):</span> {{sr_json_metadata["CustomMetaData"]["TV regularization weight lambda"]}}</li>
+          <li><span class="w3-text-orange">Optimization time step:</span> {{sr_json_metadata["CustomMetaData"]["Optimization time step"]}}</li>
+          <li><span class="w3-text-orange">Number of primal/dual iterations:</span> {{sr_json_metadata["CustomMetaData"]["Primal/dual loops"]}}</li>
+        </ul>
+      </p>
+    </div>
+    <br>
+    <div class="w3-container">
+      <p class="w3-padding-large w3-padding-32 w3-center">
+          <img src="{{sr_png_image}}">
+      </p>
+    </div>
+  </div>
+
+  <div class="w3-row w3-padding-16">
+    <div class="w3-half w3-container">
+      <h1 class="w3-text-teal">Computing Environment</h1>
+      <p>
+        <ul>
+          {% if python is defined %}
+          <li><span class="w3-text-orange">Python:</span> {{python}}</li>
+          {% endif %}
+          {% if os is defined %}
+          <li><span class="w3-text-orange">OS:</span> {{os}}</li>
+          {% endif %}
+        </ul>
+      </p>
+    </div>
+    <div class="w3-half w3-container">
+      <h1 class="w3-text-teal">Multithreading</h1>
+      <p>
+        Number of threads for:
+        <ul>
+          {% if nipype_threads is defined and nipype_threads is not none %}
+          <li><span class="w3-text-orange">Nipype:</span> {{nipype_threads}}</li>
+          {% endif %}
+          {% if openmp_threads is defined and openmp_threads is not none %}
+          <li><span class="w3-text-orange">OpenMP:</span> {{openmp_threads}}</li>
+          {% endif %}
+        </ul>
+      </p>
+    </div>
+  </div>
+
+  <footer id="myFooter">
+    <div class="w3-container w3-theme-cyan w3-padding-8">
+      <h4>Auto-generated by MIALSRTK version {{version}} using <a href="https://jinja.palletsprojects.com/en/3.0.x/">Jinja</a> {{jinja_version}}</h4>
+    </div>
+
+    <div class="w3-container w3-theme-l1">
+      <p>Copyright &copy;
+        2016-2021 Medical Image Analysis Laboratory,
+        University Hospital Center and University of Lausanne (UNIL-CHUV),
+        Switzerland, and contributors</p>
+      <p>Powered by <a href="https://www.w3schools.com/w3css/default.asp" target="_blank">w3.css</a></p>
+    </div>
+  </footer>
+
+<!-- END MAIN -->
+</div>
+
+</body>
+</html>
diff --git a/pymialsrtk/info.py b/pymialsrtk/info.py
index f4105f390..961e2cdd8 100644
--- a/pymialsrtk/info.py
+++ b/pymialsrtk/info.py
@@ -2,10 +2,10 @@
 
 _version_major = 2
 _version_minor = 0
-_version_micro = 1
+_version_micro = 2
 _version_extra = ''
 
-__release_date__ = '24.12.2020'
+__release_date__ = '23.08.2021'
 
 __minor_version__ = "%s.%s" % (_version_major,
                                _version_minor)
@@ -16,7 +16,7 @@
                               _version_extra)
 
 # __current_year__ = datetime.datetime.now().strftime("%Y")
-__current_year__ = '2020'
+__current_year__ = '2021'
 
 __author__ = 'The MIAL Super-Resolution ToolKit developers'
 __copyright__ = 'Copyright 2016-{}, Medical Image Analysis Laboratory (MIAL), Lausanne'.format(__current_year__)
@@ -34,3 +34,5 @@
 DOWNLOAD_URL = (
     'https://github.com/sebastientourbier/{name}/archive/{ver}.tar.gz'.format(
         name=__packagename__, ver=__version__))
+
+DOCKER_HUB = 'sebastientourbier/mialsuperresolutiontoolkit'
diff --git a/pymialsrtk/interfaces/postprocess.py b/pymialsrtk/interfaces/postprocess.py
index 19a6d1aca..988807d4e 100644
--- a/pymialsrtk/interfaces/postprocess.py
+++ b/pymialsrtk/interfaces/postprocess.py
@@ -1,4 +1,4 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
@@ -258,67 +258,95 @@ class FilenamesGeneration(BaseInterface):
     def _run_interface(self, runtime):
 
         for stack in self.inputs.stacks_order:
-            # print(self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_nlm_uni_bcorr_histnorm.nii.gz',
-            #       '    --->     ',
-            #       self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(self.inputs.sr_id) + '_desc-preprocSDI_T2w.nii.gz')
-            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_nlm_uni_bcorr_histnorm.nii.gz',
-                                  self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
-                                      self.inputs.sr_id) + '_desc-preprocSDI_T2w.nii.gz'))
+            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) +
+                                         '_T2w_nlm_uni_bcorr_histnorm.nii.gz',
+                                        self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
+                                        self.inputs.sr_id) + '_desc-preprocSDI_T2w.nii.gz'))
 
             if not self.inputs.use_manual_masks:
-                # print(self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_brainMask.nii.gz',
-                #       '    --->     ',
-                #       self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(self.inputs.sr_id) + '_desc-brain_mask.nii.gz')
-                self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_brainMask.nii.gz',
-                                      self.inputs.sub_ses + '_run-' + str(stack) + '_desc-brain_mask.nii.gz'))
-
-            # print(self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_nlm_uni_bcorr_histnorm.nii.gz',
-            #       '    --->     ',
-            #       self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(self.inputs.sr_id) + '_desc-preprocSR_T2w.nii.gz')
-            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_uni_bcorr_histnorm.nii.gz',
-                                  self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
-                                      self.inputs.sr_id) + '_desc-preprocSR_T2w.nii.gz'))
-
-            # print(self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_nlm_uni_bcorr_histnorm_transform_' + str(
-            #     len(self.inputs.stacks_order)) + 'V.txt',
-            #       '    --->     ',
-            #       self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
-            #           self.inputs.sr_id) + '_T2w_from-origin_to-SDI_mode-image_xfm.txt')
-            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_nlm_uni_bcorr_histnorm_transform_' + str(
-                len(self.inputs.stacks_order)) + 'V.txt',
-                                  self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
-                                      self.inputs.sr_id) + '_T2w_from-origin_to-SDI_mode-image_xfm.txt'))
-
-            # print(self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_uni_bcorr_histnorm_LRmask.nii.gz',
-            #       '    --->     ',
-            #       self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(self.inputs.sr_id) + '_T2w_desc-brain_mask.nii.gz')
-            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) + '_T2w_uni_bcorr_histnorm_LRmask.nii.gz',
-                                  self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
-                                      self.inputs.sr_id) + '_T2w_desc-brain_mask.nii.gz'))
-
-        # print('SDI_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1.nii.gz',
-        #       '    --->     ',
-        #       self.inputs.sub_ses + '_rec-SDI' + '_id-' + str(self.inputs.sr_id) + '_T2w.nii.gz')
-        self.m_substitutions.append(('SDI_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1.nii.gz',
-                              self.inputs.sub_ses + '_rec-SDI' + '_id-' + str(self.inputs.sr_id) + '_T2w.nii.gz'))
-
-        # print('SRTV_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1_gbcorr.nii.gz',
-        #       '    --->     ',
-        #       self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w.nii.gz')
-        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1_gbcorr.nii.gz',
-                              self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w.nii.gz'))
-
-        # print('SRTV_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1.json',
-        #       '    --->     ',
-        #       self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w.json')
-        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses + '_' + str(len(self.inputs.stacks_order)) + 'V_rad1.json',
-                              self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w.json'))
-
-        # print(self.inputs.sub_ses + '_T2w_uni_bcorr_histnorm_srMask.nii.gz',
-        #       '    --->     ',
-        #       self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w_desc-brain_mask.nii.gz')
+                self.m_substitutions.append(('_brainExtraction0/', ''))
+                self.m_substitutions.append(('_brainExtraction1/', ''))
+                self.m_substitutions.append(('_brainExtraction2/', ''))
+                self.m_substitutions.append(('_brainExtraction3/', ''))
+                self.m_substitutions.append(('_brainExtraction4/', ''))
+                self.m_substitutions.append(('_brainExtraction5/', ''))
+                self.m_substitutions.append(('_brainExtraction6/', ''))
+                self.m_substitutions.append(('_brainExtraction7/', ''))
+                self.m_substitutions.append(('_brainExtraction8/', ''))
+                self.m_substitutions.append(('_brainExtraction9/', ''))
+                self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) +
+                                             '_T2w_brainMask.nii.gz',
+                                            self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
+                                            self.inputs.sr_id) + '_desc-brain_mask.nii.gz'))
+
+            self.m_substitutions.append(('_T2w_desc-brain_', '_desc-brain_'))
+
+            self.m_substitutions.append(('_srtkMaskImage010/', ''))
+            self.m_substitutions.append(('_srtkMaskImage011/', ''))
+            self.m_substitutions.append(('_srtkMaskImage012/', ''))
+            self.m_substitutions.append(('_srtkMaskImage013/', ''))
+            self.m_substitutions.append(('_srtkMaskImage014/', ''))
+            self.m_substitutions.append(('_srtkMaskImage015/', ''))
+            self.m_substitutions.append(('_srtkMaskImage016/', ''))
+            self.m_substitutions.append(('_srtkMaskImage017/', ''))
+            self.m_substitutions.append(('_srtkMaskImage018/', ''))
+            self.m_substitutions.append(('_srtkMaskImage019/', ''))
+            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) +
+                                         '_T2w_uni_bcorr_histnorm.nii.gz',
+                                        self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
+                                        self.inputs.sr_id) + '_desc-preprocSR_T2w.nii.gz'))
+
+            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) +
+                                         '_T2w_nlm_uni_bcorr_histnorm_transform_' + str(
+                                        len(self.inputs.stacks_order)) + 'V.txt',
+                                        self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
+                                        self.inputs.sr_id) + '_mod-T2w_from-origin_to-SDI_mode-image_xfm.txt'))
+
+            self.m_substitutions.append((self.inputs.sub_ses + '_run-' + str(stack) +
+                                         '_T2w_uni_bcorr_histnorm_LRmask.nii.gz',
+                                        self.inputs.sub_ses + '_run-' + str(stack) + '_id-' + str(
+                                        self.inputs.sr_id) + '_desc-brain_mask.nii.gz'))
+
+        self.m_substitutions.append(('SDI_' + self.inputs.sub_ses + '_' +
+                                     str(len(self.inputs.stacks_order)) + 'V_rad1.nii.gz',
+                                     self.inputs.sub_ses + '_rec-SDI' + '_id-' +
+                                     str(self.inputs.sr_id) + '_T2w.nii.gz'))
+
+        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses + '_' +
+                                     str(len(self.inputs.stacks_order)) + 'V_rad1_gbcorr.nii.gz',
+                                    self.inputs.sub_ses + '_rec-SR' + '_id-' +
+                                     str(self.inputs.sr_id) + '_T2w.nii.gz'))
+
+        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses + '_' +
+                                     str(len(self.inputs.stacks_order)) + 'V_rad1.json',
+                                    self.inputs.sub_ses + '_rec-SR' + '_id-' +
+                                     str(self.inputs.sr_id) + '_T2w.json'))
+
         self.m_substitutions.append((self.inputs.sub_ses + '_T2w_uni_bcorr_histnorm_srMask.nii.gz',
-                                     self.inputs.sub_ses + '_rec-SR' + '_id-' + str(self.inputs.sr_id) + '_T2w_desc-brain_mask.nii.gz'))
+                                     self.inputs.sub_ses + '_rec-SR' +
+                                     '_id-' + str(self.inputs.sr_id) +
+                                     '_mod-T2w_desc-brain_mask.nii.gz'))
+
+        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses +
+                                     '_' + str(len(self.inputs.stacks_order)) +
+                                     'V_rad1_srMask.nii.gz',
+                                     self.inputs.sub_ses + '_rec-SR' +
+                                     '_id-' + str(self.inputs.sr_id) +
+                                     '_mod-T2w_desc-brain_mask.nii.gz'))
+
+        self.m_substitutions.append(('SRTV_' + self.inputs.sub_ses +
+                                     '_' + str(len(self.inputs.stacks_order)) +
+                                     'V_rad1.png',
+                                     self.inputs.sub_ses + '_rec-SR' +
+                                     '_id-' + str(self.inputs.sr_id) + '_T2w.png'))
+
+        self.m_substitutions.append(('motion_index_QC.png',
+                                     self.inputs.sub_ses + '_rec-SR' +
+                                     '_id-' + str(self.inputs.sr_id) + '_desc-motion_stats.png'))
+
+        self.m_substitutions.append(('motion_index_QC.tsv',
+                                     self.inputs.sub_ses + '_rec-SR' +
+                                     '_id-' + str(self.inputs.sr_id) + '_desc-motion_stats.tsv'))
 
         return runtime
 
@@ -329,19 +357,33 @@ def _list_outputs(self):
         return outputs
 
 
-
 def binarize_image(input_image):
+    """Binarize an image and save the mask.
+
+    Parameters
+    ----------
+    input_image : string
+        Path of input image
+
+    Returns
+    -------
+    output_mask : string
+        Absolute path of output mask
+
+    """
     import nibabel as nib
     import os
     from nipype.utils.filemanip import split_filename
 
     im = nib.load(input_image)
 
-    out = nib.Nifti1Image(dataobj=(im.get_fdata() > 0.01).astype(int), affine=im.affine)
+    out = nib.Nifti1Image(dataobj=(im.get_fdata() > 0.01).astype(int),
+                          affine=im.affine)
     out._header = im.header
 
-    _,name,ext = split_filename(input_image)
+    _, name, ext = split_filename(input_image)
     output_mask = name + '_srMask' + ext
     nib.save(filename=output_mask, img=out)
+    output_mask = os.path.abspath(output_mask)
 
-    return os.path.abspath(output_mask)
\ No newline at end of file
+    return output_mask
diff --git a/pymialsrtk/interfaces/preprocess.py b/pymialsrtk/interfaces/preprocess.py
index 020c9f437..a580850cd 100644
--- a/pymialsrtk/interfaces/preprocess.py
+++ b/pymialsrtk/interfaces/preprocess.py
@@ -1,4 +1,4 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
@@ -13,18 +13,25 @@
 import os
 import traceback
 from glob import glob
+import pathlib
 
 import numpy as np
+from traits.api import *
+
 import nibabel
 import cv2
 import skimage.measure
-
 import scipy.ndimage as snd
 from skimage import morphology
 from scipy.signal import argrelextrema
+import pandas as pd
+
+import matplotlib
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 try:
-    import tensorflow as tf
+    import tensorflow.compat.v1 as tf
 except ImportError:
     print("Tensorflow not available. Can not run brain extraction")
 
@@ -34,9 +41,6 @@
 except ImportError:
     print("tflearn not available. Can not run brain extraction")
 
-
-from traits.api import *
-
 from nipype.utils.filemanip import split_filename
 from nipype.interfaces.base import traits, \
     TraitedSpec, File, InputMultiPath, OutputMultiPath, BaseInterface, BaseInterfaceInputSpec
@@ -893,7 +897,8 @@ class FilteringByRunidOutputSpec(TraitedSpec):
 class FilteringByRunid(BaseInterface):
     """Runs a filtering of files.
 
-    This module filters the input files matching the specified run-ids. Other files are discarded.
+    This module filters the input files matching the specified run-ids.
+    Other files are discarded.
 
     Examples
     --------
@@ -943,6 +948,8 @@ class StacksOrderingOutputSpec(TraitedSpec):
     """Class used to represent outputs of the StacksOrdering interface."""
 
     stacks_order = traits.List(desc='Order of image `run-id` to be used for reconstruction')
+    motion_tsv = File(desc='Output TSV file with results used to create `report_image`')
+    report_image = File(desc='Output PNG image for report')
 
 
 class StacksOrdering(BaseInterface):
@@ -954,9 +961,20 @@ class StacksOrdering(BaseInterface):
     --------
     >>> from pymialsrtk.interfaces.preprocess import StacksOrdering
     >>> stacksOrdering = StacksOrdering()
-    >>> stacksOrdering.inputs.input_masks = ['sub-01_run-1_mask.nii.gz', 'sub-01_run-4_mask.nii.gz', 'sub-01_run-2_mask.nii.gz']
+    >>> stacksOrdering.inputs.input_masks = ['sub-01_run-1_mask.nii.gz',
+    >>>                                      'sub-01_run-4_mask.nii.gz',
+    >>>                                      'sub-01_run-2_mask.nii.gz']
     >>> stacksOrdering.run() # doctest: +SKIP
 
+    .. note:: In the case of discontinuous brain masks, the centroid coordinates of
+        the slices excluded from the mask are set to `numpy.nan` and are not
+        anymore considered in the motion index computation since `v2.0.2` release.
+        Prior to this release, the centroids of these slices were set to zero
+        that has shown to drastically increase the motion index with respect
+        to the real motion during acquisition. However the motion in the remaining
+        slices that were actually used for SR reconstruction might not correspond
+        to the high value of this index.
+
     """
 
     input_spec = StacksOrderingInputSpec
@@ -966,7 +984,7 @@ class StacksOrdering(BaseInterface):
 
     def _run_interface(self, runtime):
         try:
-            self.m_stack_order = self._compute_stack_order(self.inputs.input_masks)
+            self.m_stack_order = self._compute_stack_order()
         except Exception as e:
             print('Failed')
             print(e)
@@ -975,21 +993,26 @@ def _run_interface(self, runtime):
     def _list_outputs(self):
         outputs = self._outputs().get()
         outputs['stacks_order'] = self.m_stack_order
+        outputs['report_image'] = os.path.abspath('motion_index_QC.png')
+        outputs['motion_tsv'] = os.path.abspath('motion_index_QC.tsv')
         return outputs
 
     def _compute_motion_index(self, in_file):
         """Function to compute the motion index.
 
-        The motion index is computed from the inter-slice displacement of the centroid of the brain mask.
+        The motion index is computed from the inter-slice displacement of
+        the centroid of the brain mask.
+
         """
         central_third = True
 
         img = nibabel.load(in_file)
-
-        # Todo: Compute centroid displacement as a distance instead of a number of voxel
-        # voxelspacing = img.header['pixdim'][2]
         data = img.get_fdata()
 
+        # To compute centroid displacement as a distance
+        # instead of a number of voxel
+        sx, sy, sz = img.header.get_zooms()
+
         z = np.where(data)[2]
         data = data[..., int(min(z)):int(max(z) + 1)]
 
@@ -1003,37 +1026,237 @@ def _compute_motion_index(self, in_file):
 
         for i in range(data.shape[2]):
             moments = skimage.measure.moments(data[..., i])
-            centroid_coord[i, :] = [moments[0, 1] / moments[0, 0], moments[1, 0] / moments[0, 0]]
 
-        centroid_coord = centroid_coord[~np.isnan(centroid_coord)]
-        centroid_coord = np.reshape(centroid_coord, (int(centroid_coord.shape[0] / 2), 2))
+            try:
+                centroid_coordx = moments[0, 1] / moments[0, 0]
+                centroid_coordy = moments[1, 0] / moments[0, 0]
+            # This happens in the case of discontinuous brain masks
+            except ZeroDivisionError:
+                centroid_coordx = np.nan
+                centroid_coordy = np.nan
 
-        nSlices = data.shape[2]
-        score = (np.var(centroid_coord[:, 0]) + np.var(centroid_coord[:, 1])) / nSlices
+            centroid_coord[i, :] = [centroid_coordx, centroid_coordy]
 
-        return score
+        nb_of_notnans = np.count_nonzero(~np.isnan(centroid_coord))
+        nb_of_nans = np.count_nonzero(np.isnan(centroid_coord))
+        print(f'  Info: Number of NaNs = {nb_of_nans}')
+        prop_of_nans = nb_of_nans / (nb_of_nans + nb_of_notnans)
 
-    def _compute_stack_order(self, in_files):
+        centroid_coord = centroid_coord[~np.isnan(centroid_coord)]
+        centroid_coord = np.reshape(centroid_coord, (int(centroid_coord.shape[0] / 2), 2))
+
+        # Zero-centering
+        centroid_coord[:, 0] -= np.mean(centroid_coord[:, 0])
+        centroid_coord[:, 1] -= np.mean(centroid_coord[:, 1])
+
+        # Convert from "number of voxels" to "mm" based on the voxel size
+        centroid_coord[:, 0] *= sx
+        centroid_coord[:, 1] *= sy
+
+        nb_slices = centroid_coord.shape[0]
+        score = (np.var(centroid_coord[:, 0]) + np.var(centroid_coord[:, 1])) / ( nb_slices * sz)
+
+        return score, prop_of_nans, centroid_coord[:, 0], centroid_coord[:, 1]
+
+    def _create_report_image(self, score, prop_of_nans, centroid_coordx, centroid_coordy):
+        # Output report image basename
+        image_basename = 'motion_index_QC'
+
+        print("\t>> Create report image...")
+        # Visualization setup
+        matplotlib.use('agg')
+        sns.set_style("whitegrid")
+        sns.set(font_scale=1)
+
+        # Compute mean centroid coordinates for each image
+        mean_centroid_coordx = {}
+        mean_centroid_coordy = {}
+        for f in self.inputs.input_masks:
+            mean_centroid_coordx[f] = np.nanmean(centroid_coordx[f])
+            mean_centroid_coordy[f] = np.nanmean(centroid_coordy[f])
+
+        # Format data and create a Pandas DataFrame
+        print("\t\t\t - Format data...")
+        df_files = []
+        df_slices = []
+        df_motion_ind = []
+        df_prop_of_nans = []
+        df_centroid_coordx = []
+        df_centroid_coordy = []
+        df_centroid_displ = []
+
+        for f in self.inputs.input_masks:
+            # Extract only filename with extension from the absolute path
+            path = pathlib.Path(f)
+            # Extract the "run-xx" part in the filename
+            fname = path.stem.split('_T2w_')[0].split('_')[1]
+
+            for i, (coordx, coordy) in enumerate(zip(centroid_coordx[f], centroid_coordy[f])):
+                df_files.append(fname)
+                df_slices.append(i)
+                df_motion_ind.append(score[f])
+                df_prop_of_nans.append(prop_of_nans[f])
+                df_centroid_coordx.append(coordx)
+                df_centroid_coordy.append(coordy)
+                if not np.isnan(coordx) and not np.isnan(coordy):
+                    df_centroid_displ.append(
+                        np.sqrt(coordx * coordx + coordy * coordy)
+                    )
+                else:
+                    df_centroid_displ.append(np.nan)
+
+        # Create a dataframe to facilitate handling with the results
+        print("\t\t\t - Create DataFrame...")
+        df = pd.DataFrame(
+            {
+                "Scan": df_files,
+                "Slice": df_files,
+                "Motion Index": df_motion_ind,
+                "Proportion of NaNs (%)": df_prop_of_nans,
+                "X (mm)": df_centroid_coordx,
+                "Y (mm)": df_centroid_coordy,
+                "Displacement Magnitude (mm)": df_centroid_displ,
+            }
+        )
+        df = df.sort_values(by=['Motion Index', 'Scan', 'Slice'])
+
+        # Save the results in a TSV file
+        tsv_file = os.path.abspath('motion_index_QC.tsv')
+        print(f"\t\t\t - Save motion results to {tsv_file}...")
+        df.to_csv(tsv_file, sep='\t')
+
+        # Make multiple figures with seaborn,
+        # Saved in temporary png image and
+        # combined in a final report image
+        print("\t\t\t - Create figures...")
+
+        # Show the zero-centered positions of the centroids
+        sf0 = sns.jointplot(
+            data=df, x="X (mm)", y="Y (mm)",
+            hue="Scan",
+            height=6,
+        )
+        # Save the temporary report image
+        image_filename = os.path.abspath(image_basename + '_0.png')
+        print(f'\t\t\t - Save report image 0 as {image_filename}...')
+        sf0.savefig(image_filename, dpi=150)
+        plt.close(sf0.fig)
+
+        # Show the scan motion index
+        sf1 = sns.catplot(
+                data=df, y="Scan", x="Motion Index",
+                kind="bar"
+        )
+        sf1.ax.set_yticklabels(
+            sf1.ax.get_yticklabels(),
+            rotation=0
+        )
+        sf1.fig.set_size_inches(6, 2)
+        # Save the temporary report image
+        image_filename = os.path.abspath(image_basename + '_1.png')
+        print(f'\t\t\t - Save report image 1 as {image_filename}...')
+        sf1.savefig(image_filename, dpi=150)
+        plt.close(sf1.fig)
+
+        # Show the displacement magnitude of the centroids
+        sf2 = sns.catplot(
+                data=df, y="Scan", x="Displacement Magnitude (mm)",
+                kind="violin",
+                inner='stick'
+        )
+        sf2.ax.set_yticklabels(
+            sf2.ax.get_yticklabels(),
+            rotation=0
+        )
+        sf2.fig.set_size_inches(6, 2)
+        # Save the temporary report image
+        image_filename = os.path.abspath(image_basename + '_2.png')
+        print(f'\t\t\t - Save report image 2 as {image_filename}...')
+        sf2.savefig(image_filename, dpi=150)
+        plt.close(sf2.fig)
+
+        # Show the percentage of slice with NaNs for centroids.
+        # It can occur when the brain mask does not cover the slice
+        sf3 = sns.catplot(
+                data=df, y="Scan", x="Proportion of NaNs (%)",
+                kind="bar"
+        )
+        sf3.ax.set_yticklabels(
+            sf3.ax.get_yticklabels(),
+            rotation=0
+        )
+        sf3.fig.set_size_inches(6, 2)
+        # Save the temporary report image
+        image_filename = os.path.abspath(image_basename + '_3.png')
+        print(f'\t\t\t - Save report image 3 as {image_filename}...')
+        sf3.savefig(image_filename, dpi=150)
+        plt.close(sf3.fig)
+
+        # Define a method to load the temporary report images
+        def read_image(filename):
+            """Read the PNG image with matplotlib.
+
+            Parameters
+            ----------
+            filename : string
+                Image filename without the absolute path
+
+            """
+            return matplotlib.image.imread(os.path.abspath(filename))
+
+        # Create the final report image that combines the
+        # four temporary report images
+
+        fig = plt.figure(constrained_layout=True, figsize=(20, 10))
+
+        subfigs = fig.subfigures(1, 2)
+
+        axs = subfigs.flat[0].subplots(1, 1)
+        axs.imshow(read_image(image_basename + '_0.png'))
+        axs.set_axis_off()
+
+        axs = subfigs.flat[1].subplots(3, 1)
+        for i, ax in enumerate(axs):
+            ax.imshow(read_image(image_basename + f'_{i+1}.png'))
+            ax.set_axis_off()
+
+        # Save the final report image
+        image_filename = os.path.abspath('motion_index_QC.png')
+        print(f'\t\t\t - Save final report image as {image_filename}...')
+        plt.savefig(image_filename, dpi=150)
+
+    def _compute_stack_order(self):
         """Function to compute the stacks order.
 
-        The motion index is computed for each mask. Stacks are ordered according to their motion index.
-        When the view plane is specified in the filenames (tag `vp`), stacks are ordered such that the 3 first ones are
-        othogonal / in three different orientations.
+        The motion index is computed for each mask.
+        Stacks are ordered according to their motion index.
+        When the view plane is specified in the filenames (tag `vp`),
+        stacks are ordered such that the 3 first ones are
+        orthogonal / in three different orientations.
         """
         motion_ind = []
 
-        for f in in_files:
-            motion_ind.append(self._compute_motion_index(f))
+        score = {}
+        prop_of_nans = {}
+        centroid_coordx = {}
+        centroid_coordy = {}
 
-        vp_defined = -1 not in [f.find('vp') for f in in_files]
+        for f in self.inputs.input_masks:
+            score[f], prop_of_nans[f], centroid_coordx[f], centroid_coordy[f] = self._compute_motion_index(f)
+            motion_ind.append(score[f])
+
+        self._create_report_image(score, prop_of_nans, centroid_coordx, centroid_coordy)
+
+        vp_defined = -1 not in [f.find('vp') for f in self.inputs.input_masks]
         if vp_defined:
             orientations_ = []
-            for f in in_files:
+            for f in self.inputs.input_masks:
                 orientations_.append((f.split('_vp-')[1]).split('_')[0])
-            _, images_ordered, orientations_ordered = (list(t) for t in zip(
-                *sorted(zip(motion_ind, in_files, orientations_))))
+            _, images_ordered, orientations_ordered = (
+                list(t) for t in zip(*sorted(zip(motion_ind, self.inputs.input_masks, orientations_)))
+            )
         else:
-            _, images_ordered = (list(t) for t in zip(*sorted(zip(motion_ind, in_files))))
+            _, images_ordered = (list(t) for t in zip(*sorted(zip(motion_ind, self.inputs.input_masks))))
 
         run_order = [int(f.split('run-')[1].split('_')[0]) for f in images_ordered]
 
@@ -1162,7 +1385,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
 
         """
 
-        ##### Step 1: Brain localization #####
+        # Step 1: Brain localization
         normalize = "local_max"
         width = 128
         height = 128
@@ -1172,27 +1395,28 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
 
         img_nib = nibabel.load(os.path.join(dataPath))
         image_data = img_nib.get_data()
+        max_val = np.max(image_data)
         images = np.zeros((image_data.shape[2], width, height, n_channels))
         pred3dFinal = np.zeros((image_data.shape[2], image_data.shape[0], image_data.shape[1], n_channels))
 
         slice_counter = 0
         for ii in range(image_data.shape[2]):
-           img_patch = cv2.resize(image_data[:, :, ii], dsize=(width, height), fx=width,
-                                   fy=height)
-
-           if normalize:
-              if normalize == "local_max":
-                 images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
-              elif normalize == "global_max":
-                 images[slice_counter, :, :, 0] = img_patch / max_val
-              elif normalize == "mean_std":
-                 images[slice_counter, :, :, 0] = (img_patch-np.mean(img_patch))/np.std(img_patch)
-              else:
-                 raise ValueError('Please select a valid normalization')
-           else:
-              images[slice_counter, :, :, 0] = img_patch
-
-           slice_counter += 1
+            img_patch = cv2.resize(image_data[:, :, ii],
+                                  dsize=(width, height),
+                                  fx=width, fy=height)
+            if normalize:
+                if normalize == "local_max":
+                    images[slice_counter, :, :, 0] = img_patch / np.max(img_patch)
+                elif normalize == "global_max":
+                    images[slice_counter, :, :, 0] = img_patch / max_val
+                elif normalize == "mean_std":
+                    images[slice_counter, :, :, 0] = (img_patch - np.mean(img_patch)) / np.std(img_patch)
+                else:
+                    raise ValueError('Please select a valid normalization')
+            else:
+                images[slice_counter, :, :, 0] = img_patch
+
+            slice_counter += 1
 
         # Tensorflow graph
         g = tf.Graph()
@@ -1245,7 +1469,6 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
         # Thresholding parameter to binarize predictions
         percentileLoc = thresholdLoc*100
 
-        im = np.zeros((1, width, height, n_channels))
         pred3d = []
         with tf.Session(graph=g) as sess_test_loc:
             # Restore the model
@@ -1263,7 +1486,6 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
                 pred_bin = np.where(pred_ > theta, 1, 0)
                 pred3d.append(pred_bin[0, :, :, 0].astype('float64'))
 
-            #####
             pred3d = np.asarray(pred3d)
             heights = []
             widths = []
@@ -1297,7 +1519,7 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             y_beg = med_y-half_max_y-border_y
             y_end = med_y+half_max_y+border_y
 
-        ##### Step 2: Brain segmentation #####
+        # Step 2: Brain segmentation
         width = 96
         height = 96
 
@@ -1372,7 +1594,6 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
             tf_saver.restore(sess_test_seg, modelCkptSeg)
 
             for idx in range(images.shape[0]):
-
                 im = np.reshape(images[idx, :, :], [1, width, height, n_channels])
                 feed_dict = {x: im}
                 pred_ = sess_test_seg.run(pred, feed_dict=feed_dict)
@@ -1380,18 +1601,27 @@ def _extractBrain(self, dataPath, modelCkptLoc, thresholdLoc, modelCkptSeg, thre
                 theta = np.percentile(pred_, percentileSeg)
                 pred_bin = np.where(pred_ > theta, 1, 0)
                 # Map predictions to original indices and size
-                pred_bin = cv2.resize(pred_bin[0, :, :, 0], dsize=(y_end-y_beg, x_end-x_beg), interpolation=cv2.INTER_NEAREST)
+                pred_bin = cv2.resize(
+                    pred_bin[0, :, :, 0],
+                    dsize=(y_end-y_beg, x_end-x_beg),
+                    interpolation=cv2.INTER_NEAREST)
                 pred3dFinal[idx, x_beg:x_end, y_beg:y_end,0] = pred_bin.astype('float64')
 
             pppp = True
             if pppp:
                 pred3dFinal = self._post_processing(np.asarray(pred3dFinal))
-            pred3d = [cv2.resize(elem, dsize=(image_data.shape[1], image_data.shape[0]), interpolation=cv2.INTER_NEAREST) for elem in pred3dFinal]
+            pred3d = [
+                cv2.resize(
+                    elem,
+                    dsize=(image_data.shape[1], image_data.shape[0]),
+                    interpolation=cv2.INTER_NEAREST
+                ) for elem in pred3dFinal
+            ]
             pred3d = np.asarray(pred3d)
-            upsampled = np.swapaxes(np.swapaxes(pred3d,1,2),0,2) #if Orient module applied, no need for this line(?)
-            up_mask = nibabel.Nifti1Image(upsampled,img_nib.affine)
-            # Save output mask
+            upsampled = np.swapaxes(np.swapaxes(pred3d, 1, 2), 0, 2)  # if Orient module applied, no need for this line(?)
+            up_mask = nibabel.Nifti1Image(upsampled, img_nib.affine)
 
+            # Save output mask
             save_file = self._gen_filename('out_file')
             nibabel.save(up_mask, save_file)
 
@@ -1413,169 +1643,144 @@ def _extractLargestCC(self, image):
         largest_cc[output == max_label] = 255
         return largest_cc.astype('uint8')
 
-    def _post_processing(self, pred_lbl):
+    def _post_processing(self, pred_lbl, verbose=False):
         """Post-processing the binarized network output by Priscille de Dumast."""
 
-        # post_proc = True
         post_proc_cc = True
         post_proc_fill_holes = True
 
         post_proc_closing_minima = True
         post_proc_opening_maxima = True
         post_proc_extremity = False
-        # stackmodified = True
 
         crt_stack = pred_lbl.copy()
         crt_stack_pp = crt_stack.copy()
 
-        if 1:
-
-            distrib = []
-            for iSlc in range(crt_stack.shape[0]):
-                distrib.append(np.sum(crt_stack[iSlc]))
-
-            if post_proc_cc:
-                # print("post_proc_cc")
-                crt_stack_cc = crt_stack.copy()
-                labeled_array, _ = snd.measurements.label(crt_stack_cc)
-                unique, counts = np.unique(labeled_array, return_counts=True)
-
-                # Try to remove false positives seen as independent connected components #2ndBrain
-                for ind, _ in enumerate(unique):
-                    if 5 < counts[ind] and counts[ind] < 300:
-                        wherr = np.where(labeled_array == unique[ind])
-                        for ii in range(len(wherr[0])):
-                            crt_stack_cc[wherr[0][ii], wherr[1][ii], wherr[2][ii]] = 0
-
-                crt_stack_pp = crt_stack_cc.copy()
-
-            if post_proc_fill_holes:
-                # print("post_proc_fill_holes")
-                crt_stack_holes = crt_stack_pp.copy()
-
-                inv_mask = 1 - crt_stack_holes
-                labeled_holes, _ = snd.measurements.label(inv_mask)
-                unique, counts = np.unique(labeled_holes, return_counts=True)
-
-                for lbl in unique[2:]:
-                    trou = np.where(labeled_holes == lbl)
-                    for ind in range(len(trou[0])):
-                        inv_mask[trou[0][ind], trou[1][ind], trou[2][ind]] = 0
-
-                crt_stack_holes = 1 - inv_mask
-                crt_stack_cc = crt_stack_holes.copy()
-                crt_stack_pp = crt_stack_holes.copy()
+        distrib = []
+        for iSlc in range(crt_stack.shape[0]):
+            distrib.append(np.sum(crt_stack[iSlc]))
+
+        if post_proc_cc:
+            crt_stack_cc = crt_stack.copy()
+            labeled_array, _ = snd.measurements.label(crt_stack_cc)
+            unique, counts = np.unique(labeled_array, return_counts=True)
+
+            # Try to remove false positives seen as independent connected components #2ndBrain
+            for ind, _ in enumerate(unique):
+                if 5 < counts[ind] < 300:
+                    wherr = np.where(labeled_array == unique[ind])
+                    for ii in range(len(wherr[0])):
+                        crt_stack_cc[wherr[0][ii], wherr[1][ii], wherr[2][ii]] = 0
+
+            crt_stack_pp = crt_stack_cc.copy()
+
+        if post_proc_fill_holes:
+            crt_stack_holes = crt_stack_pp.copy()
+
+            inv_mask = 1 - crt_stack_holes
+            labeled_holes, _ = snd.measurements.label(inv_mask)
+            unique, counts = np.unique(labeled_holes, return_counts=True)
+
+            for lbl in unique[2:]:
+                trou = np.where(labeled_holes == lbl)
+                for ind in range(len(trou[0])):
+                    inv_mask[trou[0][ind], trou[1][ind], trou[2][ind]] = 0
+
+            crt_stack_holes = 1 - inv_mask
+            crt_stack_pp = crt_stack_holes.copy()
+
+            distrib_cc = []
+            for iSlc in range(crt_stack_pp.shape[0]):
+                distrib_cc.append(np.sum(crt_stack_pp[iSlc]))
+
+        if post_proc_closing_minima or post_proc_opening_maxima:
+
+            if post_proc_closing_minima:
+                crt_stack_closed_minima = crt_stack_pp.copy()
+
+                # for local minima
+                local_minima = argrelextrema(np.asarray(distrib_cc), np.less)[0]
+                local_maxima = argrelextrema(np.asarray(distrib_cc), np.greater)[0]
+
+                for iMin, _ in enumerate(local_minima):
+                    for iMax in range(len(local_maxima) - 1):
+                        # find between which maxima is the minima localized
+                        if local_maxima[iMax] < local_minima[iMin] < local_maxima[iMax + 1]:
+                            # check if diff max-min is large enough to be considered
+                            if ((distrib_cc[local_maxima[iMax]] - distrib_cc[local_minima[iMin]] > 50) and
+                               (distrib_cc[local_maxima[iMax + 1]] - distrib_cc[local_minima[iMin]] > 50)):
+                                sub_stack = crt_stack_closed_minima[local_maxima[iMax] - 1:local_maxima[iMax + 1] + 1, :, :]
+                                sub_stack = morphology.binary_closing(sub_stack)
+                                crt_stack_closed_minima[local_maxima[iMax] - 1:local_maxima[iMax + 1] + 1, :, :] = sub_stack
+                crt_stack_pp = crt_stack_closed_minima.copy()
+
+                distrib_closed = []
+                for iSlc in range(crt_stack_closed_minima.shape[0]):
+                    distrib_closed.append(np.sum(crt_stack_closed_minima[iSlc]))
+
+            if post_proc_opening_maxima:
+                crt_stack_opened_maxima = crt_stack_pp.copy()
+
+                local = True
+                if local:
+                    local_maxima_n = argrelextrema(
+                        np.asarray(distrib_closed), np.greater
+                    )[0]  # default is mode='clip'. Doesn't consider extremity as being an extrema
+
+                    for iMax, _ in enumerate(local_maxima_n):
+                        # Check if this local maxima is a "peak"
+                        if ((distrib[local_maxima_n[iMax]] - distrib[local_maxima_n[iMax] - 1] > 50) and
+                           (distrib[local_maxima_n[iMax]] - distrib[local_maxima_n[iMax] + 1] > 50)):
+
+                            if verbose:
+                                print("Ceci est un pic de au moins 50.",
+                                      distrib[local_maxima_n[iMax]],
+                                      "en",
+                                      local_maxima_n[iMax])
+                                print("                                bordé de",
+                                      distrib[local_maxima_n[iMax] - 1],
+                                      "en",
+                                      local_maxima_n[iMax] - 1)
+                                print("                                et",
+                                      distrib[local_maxima_n[iMax] + 1],
+                                      "en",
+                                      local_maxima_n[iMax] + 1)
+                                print("")
+
+                            sub_stack = crt_stack_opened_maxima[local_maxima_n[iMax] - 1:local_maxima_n[iMax] + 2, :, :]
+                            sub_stack = morphology.binary_opening(sub_stack)
+                            crt_stack_opened_maxima[local_maxima_n[iMax] - 1:local_maxima_n[iMax] + 2, :, :] = sub_stack
+                else:
+                    crt_stack_opened_maxima = morphology.binary_opening(crt_stack_opened_maxima)
+                crt_stack_pp = crt_stack_opened_maxima.copy()
 
-                distrib_cc = []
+                distrib_opened = []
                 for iSlc in range(crt_stack_pp.shape[0]):
-                    distrib_cc.append(np.sum(crt_stack_pp[iSlc]))
-
-            if post_proc_closing_minima or post_proc_opening_maxima:
-
-                if 0:  # closing GLOBAL
-                    crt_stack_closed_minima = crt_stack_pp.copy()
-                    crt_stack_closed_minima = morphology.binary_closing(crt_stack_closed_minima)
-                    crt_stack_pp = crt_stack_closed_minima.copy()
-
-                    distrib_closed = []
-                    for iSlc in range(crt_stack_closed_minima.shape[0]):
-                        distrib_closed.append(np.sum(crt_stack_closed_minima[iSlc]))
-
-                if post_proc_closing_minima:
-                    # if 0:
-                    crt_stack_closed_minima = crt_stack_pp.copy()
-
-                    # for local minima
-                    local_minima = argrelextrema(np.asarray(distrib_cc), np.less)[0]
-                    local_maxima = argrelextrema(np.asarray(distrib_cc), np.greater)[0]
-
-                    for iMin, _ in enumerate(local_minima):
-                        for iMax in range(len(local_maxima) - 1):
-                            # print(local_maxima[iMax], "<", local_minima[iMin], "AND", local_minima[iMin], "<", local_maxima[iMax+1], "   ???")
-
-                            # find between which maxima is the minima localized
-                            if local_maxima[iMax] < local_minima[iMin] and local_minima[iMin] < local_maxima[iMax + 1]:
-
-                                # check if diff max-min is large enough to be considered
-                                if ((distrib_cc[local_maxima[iMax]] - distrib_cc[local_minima[iMin]] > 50) and
-                                   (distrib_cc[local_maxima[iMax + 1]] - distrib_cc[local_minima[iMin]] > 50)):
-                                    sub_stack = crt_stack_closed_minima[local_maxima[iMax] - 1:local_maxima[iMax + 1] + 1, :, :]
+                    distrib_opened.append(np.sum(crt_stack_pp[iSlc]))
 
-                                    # print("We did 3d close.")
-                                    sub_stack = morphology.binary_closing(sub_stack)
-                                    crt_stack_closed_minima[local_maxima[iMax] - 1:local_maxima[iMax + 1] + 1, :, :] = sub_stack
+            if post_proc_extremity:
+                crt_stack_extremity = crt_stack_pp.copy()
 
-                    crt_stack_pp = crt_stack_closed_minima.copy()
+                # check si y a un maxima sur une extremite
+                maxima_extrema = argrelextrema(np.asarray(distrib_closed),
+                                               np.greater,
+                                               mode='wrap')[0]
 
-                    distrib_closed = []
-                    for iSlc in range(crt_stack_closed_minima.shape[0]):
-                        distrib_closed.append(np.sum(crt_stack_closed_minima[iSlc]))
+                if distrib_opened[0] - distrib_opened[1] > 40:
+                    sub_stack = crt_stack_extremity[0:2, :, :]
+                    sub_stack = morphology.binary_opening(sub_stack)
+                    crt_stack_extremity[0:2, :, :] = sub_stack
 
-                if post_proc_opening_maxima:
-                    crt_stack_opened_maxima = crt_stack_pp.copy()
+                if pred_lbl.shape[0] - 1 in maxima_extrema:
+                    sub_stack = crt_stack_opened_maxima[-2:, :, :]
+                    sub_stack = morphology.binary_opening(sub_stack)
+                    crt_stack_opened_maxima[-2:, :, :] = sub_stack
 
-                    local = True
-                    if local:
-                        local_maxima_n = argrelextrema(np.asarray(distrib_closed), np.greater)[
-                            0]  # default is mode='clip'. Doesn't consider extremity as being an extrema
-
-                        for iMax, _ in enumerate(local_maxima_n):
-
-                            # Check if this local maxima is a "peak"
-                            if ((distrib[local_maxima_n[iMax]] - distrib[local_maxima_n[iMax] - 1] > 50) and
-                               (distrib[local_maxima_n[iMax]] - distrib[local_maxima_n[iMax] + 1] > 50)):
-
-                                if 0:
-                                    print("Ceci est un pic de au moins 50.", distrib[local_maxima_n[iMax]], "en",
-                                          local_maxima_n[iMax])
-                                    print("                                bordé de", distrib[local_maxima_n[iMax] - 1],
-                                          "en", local_maxima_n[iMax] - 1)
-                                    print("                                et", distrib[local_maxima_n[iMax] + 1], "en",
-                                          local_maxima_n[iMax] + 1)
-                                    print("")
-
-                                sub_stack = crt_stack_opened_maxima[local_maxima_n[iMax] - 1:local_maxima_n[iMax] + 2, :, :]
-                                sub_stack = morphology.binary_opening(sub_stack)
-                                crt_stack_opened_maxima[local_maxima_n[iMax] - 1:local_maxima_n[iMax] + 2, :, :] = sub_stack
-                    else:
-                        crt_stack_opened_maxima = morphology.binary_opening(crt_stack_opened_maxima)
-
-                    crt_stack_pp = crt_stack_opened_maxima.copy()
-
-                    distrib_opened = []
-                    for iSlc in range(crt_stack_pp.shape[0]):
-                        distrib_opened.append(np.sum(crt_stack_pp[iSlc]))
-
-                if post_proc_extremity:
-
-                    crt_stack_extremity = crt_stack_pp.copy()
-
-                    # check si y a un maxima sur une extremite
-                    maxima_extrema = argrelextrema(np.asarray(distrib_closed), np.greater, mode='wrap')[0]
-                    # print("maxima_extrema", maxima_extrema, "     numslices",numslices, "     numslices-1",numslices-1)
-
-                    if distrib_opened[0] - distrib_opened[1] > 40:
-                        # print("First slice of ", distrib_opened, " is a maxima")
-                        sub_stack = crt_stack_extremity[0:2, :, :]
-                        sub_stack = morphology.binary_opening(sub_stack)
-                        crt_stack_extremity[0:2, :, :] = sub_stack
-                        # print("On voulait close 1st slices",  sub_stack.shape[0])
-
-                    if pred_lbl.shape[0] - 1 in maxima_extrema:
-                        # print(numslices-1, "in maxima_extrema", maxima_extrema )
-
-                        sub_stack = crt_stack_opened_maxima[-2:, :, :]
-                        sub_stack = morphology.binary_opening(sub_stack)
-                        crt_stack_opened_maxima[-2:, :, :] = sub_stack
-
-                        # print("On voulait close last slices",  sub_stack.shape[0])
-
-                    crt_stack_pp = crt_stack_extremity.copy()
-
-                    distrib_opened_border = []
-                    for iSlc in range(crt_stack_pp.shape[0]):
-                        distrib_opened_border.append(np.sum(crt_stack_pp[iSlc]))
+                crt_stack_pp = crt_stack_extremity.copy()
 
+                distrib_opened_border = []
+                for iSlc in range(crt_stack_pp.shape[0]):
+                    distrib_opened_border.append(np.sum(crt_stack_pp[iSlc]))
         return crt_stack_pp
 
     def _list_outputs(self):
diff --git a/pymialsrtk/interfaces/reconstruction.py b/pymialsrtk/interfaces/reconstruction.py
index 14f93a9ba..480b54dc9 100644
--- a/pymialsrtk/interfaces/reconstruction.py
+++ b/pymialsrtk/interfaces/reconstruction.py
@@ -1,12 +1,10 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
 """PyMIALSRTK reconstruction functions."""
 
 import os
-
-from glob import glob
 import json
 
 from traits.api import *
@@ -15,6 +13,11 @@
 from nipype.interfaces.base import traits, \
     TraitedSpec, File, InputMultiPath, OutputMultiPath, BaseInterface, BaseInterfaceInputSpec
 
+import numpy as np
+import nibabel as nib
+from matplotlib import pyplot as plt
+from nilearn.plotting import plot_anat
+
 from pymialsrtk.interfaces.utils import run, reorder_by_run_ids
 
 
@@ -53,7 +56,6 @@ class MialsrtkImageReconstructionInputSpec(BaseInterfaceInputSpec):
                                     usedefault=True)
     stacks_order = traits.List(mandatory=True,
                                desc='List of stack run-id that specify the order of the stacks')
-
     no_reg = traits.Bool(default=False, desc="Skip slice-to-volume registration.")
 
 
@@ -65,26 +67,36 @@ class MialsrtkImageReconstructionOutputSpec(TraitedSpec):
 
 
 class MialsrtkImageReconstruction(BaseInterface):
-    """Creates a high resolution image from a set of low resolution images [1]_.
+    """Creates a high-resolution image from a set of low resolution images.
+
+    It is built on the BTK implementation and implements the method, presented in [Rousseau2006]_,
+    that iterates between slice-to-volume registration and reconstruction of a
+    high-resolution image using scattered data interpolation. The method converges
+    when the mean square error between the high-resolution images reconstructed in
+    the last two iterations is lower than ``1e-6``.
+
+    It is used to estimate slice motion prior to :class:`~pymialsrtk.reconstruction.MialsrtkTVSuperResolution`.
 
     References
     ------------
-    .. [1] Tourbier et al.; NeuroImage, 2015. `(link to paper) <https://doi.org/10.1016/j.neuroimage.2015.06.018>`_
+    .. [Rousseau2006] Rousseau et al.; Acad Radiol., 2006. `(link to paper) <https://doi.org/10.1016/j.acra.2006.05.003>`_
 
     Example
     ----------
     >>> from pymialsrtk.interfaces.reconstruction import MialsrtkImageReconstruction
-    >>> srtkImageReconstruction = MialsrtkTVSuperResolution()
+    >>> srtkImageReconstruction = MialsrtkImageReconstruction()
     >>> srtkImageReconstruction.inputs.bids_dir = '/my_directory'
-    >>> srtkImageReconstruction.input_images = ['sub-01_ses-01_run-1_T2w.nii.gz', 'sub-01_ses-01_run-2_T2w.nii.gz', \
-    'sub-01_ses-01_run-3_T2w.nii.gz', 'sub-01_ses-01_run-4_T2w.nii.gz']
-    >>> srtkImageReconstruction.input_masks = ['sub-01_ses-01_run-1_mask.nii.gz', 'sub-01_ses-01_run-2_mask.nii.gz', \
-    'sub-01_ses-01_run-3_mask.nii.gz', 'sub-01_ses-01_run-4_mask.nii.gz']
+    >>> srtkImageReconstruction.input_images = ['sub-01_ses-01_run-1_T2w.nii.gz',
+    >>>                                         'sub-01_ses-01_run-2_T2w.nii.gz',
+    >>>                                         'sub-01_ses-01_run-3_T2w.nii.gz',
+    >>>                                         'sub-01_ses-01_run-4_T2w.nii.gz']
+    >>> srtkImageReconstruction.input_masks = ['sub-01_ses-01_run-1_mask.nii.gz',
+    >>>                                        'sub-01_ses-01_run-2_mask.nii.gz',
+    >>>                                        'sub-01_ses-01_run-3_mask.nii.gz',
+    >>>                                        'sub-01_ses-01_run-4_mask.nii.gz']
     >>> srtkImageReconstruction.inputs.stacks_order = [3,1,2,4]
     >>> srtkImageReconstruction.inputs.sub_ses = 'sub-01_ses-01'
     >>> srtkImageReconstruction.inputs.in_roi = 'mask'
-    >>> srtkImageReconstruction.inputs.in_deltat = 0.01
-    >>> srtkImageReconstruction.inputs.in_lambda = 0.75
     >>> srtkImageReconstruction.run()  # doctest: +SKIP
 
     """
@@ -96,29 +108,26 @@ def _gen_filename(self, orig, name):
         if name == 'output_sdi':
             _, _, ext = split_filename(orig)
             output = ''.join([self.inputs.out_sdi_prefix, self.inputs.sub_ses, '_',
-                      str(len(self.inputs.stacks_order)), 'V_rad',
-                      str(int(self.inputs.input_rad_dilatation)), ext])
+                              str(len(self.inputs.stacks_order)), 'V_rad',
+                              str(int(self.inputs.input_rad_dilatation)), ext])
             return os.path.abspath(output)
 
         elif name == 'output_transforms':
             _, name, _ = split_filename(orig)
             output = ''.join([name, self.inputs.out_transf_postfix, '_',
-                     str(len(self.inputs.stacks_order)), 'V', '.txt'])
+                              str(len(self.inputs.stacks_order)), 'V', '.txt'])
 
             return os.path.abspath(output)
         return None
 
     def _run_interface(self, runtime):
-        params = []
-        params.append(''.join(["--", self.inputs.in_roi]))
+        params = [''.join(["--", self.inputs.in_roi])]
 
         input_images = reorder_by_run_ids(self.inputs.input_images, self.inputs.stacks_order)
         input_masks = reorder_by_run_ids(self.inputs.input_masks, self.inputs.stacks_order)
 
         for in_image, in_mask in zip(input_images, input_masks):
 
-            transf_file = self._gen_filename(in_image, 'output_transforms')
-
             params.append("-i")
             params.append(in_image)
 
@@ -126,11 +135,11 @@ def _run_interface(self, runtime):
                 params.append("-m")
                 params.append(in_mask)
 
+            transf_file = self._gen_filename(in_image, 'output_transforms')
             params.append("-t")
             params.append(transf_file)
 
         out_file = self._gen_filename(self.inputs.input_images[0], 'output_sdi')
-
         params.append("-o")
         params.append(out_file)
 
@@ -143,7 +152,7 @@ def _run_interface(self, runtime):
         try:
             print('... cmd: {}'.format(cmd))
             cmd = ' '.join(cmd)
-            run(cmd, env={}, cwd=os.path.abspath(self.inputs.bids_dir))
+            run(cmd, cwd=os.path.abspath(self.inputs.bids_dir))
         except Exception as e:
             print('Failed')
             print(e)
@@ -151,7 +160,9 @@ def _run_interface(self, runtime):
 
     def _list_outputs(self):
         outputs = self._outputs().get()
-        outputs['output_transforms'] = [self._gen_filename(in_image, 'output_transforms') for in_image in self.inputs.input_images]
+        outputs['output_transforms'] = [
+            self._gen_filename(in_image, 'output_transforms') for in_image in self.inputs.input_images
+        ]
         outputs['output_sdi'] = self._gen_filename(self.inputs.input_images[0], 'output_sdi')
         return outputs
 
@@ -176,14 +187,12 @@ class MialsrtkTVSuperResolutionInputSpec(BaseInterfaceInputSpec):
     deblurring = traits.Bool(False,
                              desc='Flag to set deblurring PSF during SR (double the neighborhood)',
                              usedefault=True)
-
     in_loop = traits.Int(mandatory=True,
                          desc='Number of loops (SR/denoising)')
     in_deltat = traits.Float(mandatory=True,
                              desc='Parameter deltat of TV optimizer')
     in_lambda = traits.Float(mandatory=True,
                              desc='TV regularization factor which weights the data fidelity term in TV optimizer')
-
     in_bregman_loop = traits.Int(1,
                                  desc='Number of Bregman loops',
                                  usedefault=True)
@@ -202,20 +211,17 @@ class MialsrtkTVSuperResolutionInputSpec(BaseInterfaceInputSpec):
     in_outer_thresh = traits.Float(0.000001,
                                    desc='Outer loop convergence threshold',
                                    usedefault=True)
-
     out_prefix = traits.Str("SRTV_",
                             desc='Prefix added to construct output super-resolution filename',
                             usedefault=True)
     stacks_order = traits.List(mandatory=False,
                                desc='List of stack run-id that specify the order of the stacks')
-
     input_rad_dilatation = traits.Float(1.0,
                                         desc='Radius dilatation used in prior step to construct output filename',
                                         usedefault=True)
     sub_ses = traits.Str("x",
                          desc='Subject and session BIDS identifier to construct output filename',
                          usedefault=True)
-
     use_manual_masks = traits.Bool(False,
                                    desc='Use masks of input files',
                                    usedefault=True)
@@ -225,16 +231,29 @@ class MialsrtkTVSuperResolutionOutputSpec(TraitedSpec):
     """Class used to represent outputs of the MialsrtkTVSuperResolution interface."""
 
     output_sr = File(desc='Output super-resolution image file')
-    # output_dict = Dict(desc='Super-resolution reconstruction parameters summarized in a python dictionary')
-    output_json_path = File(desc='Output path where `output_dict` should be saved ')
+    output_sr_png = File(desc='Output super-resolution PNG image file for quality assessment')
+    output_json_path = File(desc='Output json file where super-resolution reconstruction parameters '
+                                 'are summarized')
 
 
 class MialsrtkTVSuperResolution(BaseInterface):
-    """Apply super-resolution algorithm using one or multiple input images [1]_.
+    """Apply super-resolution algorithm using one or multiple input images.
+
+    It implements the super-resolution algorithm with Total-Variation regularization presented in [Tourbier2015]_.
+
+    Taking as input the list of input low resolution images and the list of slice-by-slice
+    transforms estimated with :class:`~pymialsrtk.reconstruction.MialsrtkImageReconstruction`,
+    it builds the forward model `H` that relates the low resolution images `y` to the high resolution
+    `x` to be estimated by `y = Hx`, and it solves optimally using convex optimization
+    the inverse (i.e. super-resolution) problem (finding `x`) with exact Total-Variation regularization.
+
+    The weight of TV regularization is controlled by `in_lambda`. The lower it is, the lower
+    will be the weight of the data fidelity and so the higher will the regularization.
+    The optimization time step is controlled by `in_deltat` that defines the time step of the optimizer.
 
     References
     ------------
-    .. [1] Tourbier et al.; NeuroImage, 2015. `(link to paper) <https://doi.org/10.1016/j.neuroimage.2015.06.018>`_
+    .. [Tourbier2015] Tourbier et al.; NeuroImage, 2015. `(link to paper) <https://doi.org/10.1016/j.neuroimage.2015.06.018>`_
 
     Example
     ----------
@@ -263,19 +282,28 @@ class MialsrtkTVSuperResolution(BaseInterface):
     m_out_files = ''
     m_output_dict = {}
 
-
     def _gen_filename(self, name):
         if name == 'output_sr':
             _, _, ext = split_filename(self.inputs.input_sdi)
-            output = ''.join([self.inputs.out_prefix, self.inputs.sub_ses, '_',
-                                                      str(len(self.inputs.stacks_order)), 'V_rad',
-                                                      str(int(self.inputs.input_rad_dilatation)), ext])
+            output = ''.join([self.inputs.out_prefix,
+                              self.inputs.sub_ses, '_',
+                              str(len(self.inputs.stacks_order)), 'V_rad',
+                              str(int(self.inputs.input_rad_dilatation)), ext])
             return os.path.abspath(output)
 
         elif name == 'output_json_path':
-            output = ''.join([self.inputs.out_prefix, self.inputs.sub_ses, '_',
-                                                      str(len(self.inputs.stacks_order)), 'V_rad',
-                                                      str(int(self.inputs.input_rad_dilatation)), '.json'])
+            output = ''.join([self.inputs.out_prefix,
+                              self.inputs.sub_ses, '_',
+                              str(len(self.inputs.stacks_order)), 'V_rad',
+                              str(int(self.inputs.input_rad_dilatation)), '.json'])
+
+            return os.path.abspath(output)
+
+        elif name == 'output_sr_png':
+            output = ''.join([self.inputs.out_prefix,
+                              self.inputs.sub_ses, '_',
+                              str(len(self.inputs.stacks_order)), 'V_rad',
+                              str(int(self.inputs.input_rad_dilatation)), '.png'])
 
             return os.path.abspath(output)
 
@@ -294,9 +322,9 @@ def _run_interface(self, runtime):
             cmd += ['-m', in_mask]
             cmd += ['-t', in_transform]
 
-        out_sr = self._gen_filename('output_sr')
-
         cmd += ['-r', self.inputs.input_sdi]
+
+        out_sr = self._gen_filename('output_sr')
         cmd += ['-o', out_sr]
 
         if self.inputs.deblurring:
@@ -305,7 +333,6 @@ def _run_interface(self, runtime):
         cmd += ['--loop', str(self.inputs.in_loop)]
         cmd += ['--deltat', str(self.inputs.in_deltat)]
         cmd += ['--lambda', str(self.inputs.in_lambda)]
-
         cmd += ['--bregman-loop', str(self.inputs.in_bregman_loop)]
         cmd += ['--iter', str(self.inputs.in_iter)]
         cmd += ['--step-scale', str(self.inputs.in_step_scale)]
@@ -313,7 +340,17 @@ def _run_interface(self, runtime):
         cmd += ['--inner-thresh', str(self.inputs.in_inner_thresh)]
         cmd += ['--outer-thresh', str(self.inputs.in_outer_thresh)]
 
-        # JSON file SRTV
+        try:
+            cmd = ' '.join(cmd)
+            run(cmd, cwd=os.path.abspath(self.inputs.bids_dir))
+
+        except Exception as e:
+            print('Failed')
+            print(e)
+
+        ################################################
+        # Creation of JSON sidecar file of the SR image
+        ################################################
         self.m_output_dict["Description"] = "Isotropic high-resolution image reconstructed using the Total-Variation" \
                                             " Super-Resolution algorithm provided by MIALSRTK"
         self.m_output_dict["Input sources run order"] = self.inputs.stacks_order
@@ -324,27 +361,90 @@ def _run_interface(self, runtime):
         self.m_output_dict["CustomMetaData"]["Optimization time step"] = self.inputs.in_deltat
         self.m_output_dict["CustomMetaData"]["Primal/dual loops"] = self.inputs.in_loop
 
-
         output_json_path = self._gen_filename('output_json_path')
         with open(output_json_path, 'w') as outfile:
+            print('  > Write JSON side-car...')
             json.dump(self.m_output_dict, outfile, indent=4)
-            print('json dumped.')
-
-        try:
-            cmd = ' '.join(cmd)
-            run(cmd, env={}, cwd=os.path.abspath(self.inputs.bids_dir))
 
-        except Exception as e:
-            print('Failed')
-            print(e)
+        #########################################################
+        # Save cuts of the SR image in a PNG for later reporting
+        #########################################################
+        out_sr_png = self._gen_filename('output_sr_png')
+
+        # Load the super-resolution image
+        print(f'  > Load SR image {out_sr}...')
+        img = nib.load(out_sr)
+        # Get image properties
+        zooms = img.header.get_zooms()  # zooms are the size of the voxels
+        shapes = img.shape
+        fovs = np.array(zooms) * np.array(shapes)
+        # Get middle cut
+        cuts = [s // 2 for s in shapes]
+        print(f'    Image properties: Zooms={zooms}/ Shape={shapes}/ FOV={fovs}/ middle cut={cuts}')
+
+        # Crop the image if the FOV exceeds a certain value
+        def compute_axis_crop_indices(cut, fov, max_fov=120):
+            """Compute the cropping index in a dimension if the Field-Of-View exceeds a maximum value of 120mm by default.
+
+            Parameters
+            ----------
+            cut: int
+                Middle slice index in a given dimension
+
+            fov: float
+                Slice Field-of-View (mm) in a given dimension
+
+            max_fov: float
+                Maximum Slice Field-of-View (mm) to which the image does not need to be cropped
+                (120mm by default)
+
+            Returns
+            -------
+            (crop_start_index, crop_end_index): (int, int)
+                Starting and ending indices of the image crop along the given dimension
+
+            """
+            crop_start_index = cut - max_fov // 2 if fov > max_fov else 0
+            crop_end_index = cut + max_fov // 2 if fov > max_fov else -1
+            return crop_start_index, crop_end_index
+
+        crop_start_x, crop_end_x = compute_axis_crop_indices(cuts[0], fovs[0], max_fov=120)
+        crop_start_y, crop_end_y = compute_axis_crop_indices(cuts[1], fovs[1], max_fov=120)
+        crop_start_z, crop_end_z = compute_axis_crop_indices(cuts[2], fovs[2], max_fov=120)
+
+        print(f'  > Crop SR image at '
+              f'({crop_start_x}:{crop_end_x}, '
+              f'{crop_start_y}:{crop_end_y}, '
+              f'{crop_start_z}:{crop_end_z})...')
+        cropped_img = img.slicer[
+            crop_start_x:crop_end_x,
+            crop_start_y:crop_end_y,
+            crop_start_z:crop_end_z
+        ]
+        del img
+
+        # Create and save the figure
+        plot_anat(
+            anat_img=cropped_img,
+            annotate=True,
+            draw_cross=False,
+            black_bg=True,
+            dim='auto',
+            display_mode='ortho',
+        )
+        print(f'Save the PNG image cuts as {out_sr_png}')
+        plt.savefig(
+            out_sr_png,
+            dpi=100,
+            facecolor='k',
+            edgecolor='k'
+        )
 
         return runtime
 
     def _list_outputs(self):
         outputs = self._outputs().get()
-        _, _, ext = split_filename(os.path.abspath(self.inputs.input_sdi))
         outputs['output_sr'] = self._gen_filename('output_sr')
-        # outputs['output_dict'] = self.m_output_dict
+        outputs['output_sr_png'] = self._gen_filename('output_sr_png')
         outputs['output_json_path'] = self._gen_filename('output_json_path')
-
         return outputs
diff --git a/pymialsrtk/interfaces/utils.py b/pymialsrtk/interfaces/utils.py
index 4c101d4f5..f669a8029 100644
--- a/pymialsrtk/interfaces/utils.py
+++ b/pymialsrtk/interfaces/utils.py
@@ -1,4 +1,4 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
@@ -15,13 +15,13 @@ def run(command, env=None, cwd=None):
 
     Parameters
     ----------
-    command <string>
+    command : string
         String containing the command to be executed (required)
 
-    env <os.environ>
+    env : os.environ
         Specify a custom os.environ
 
-    cwd <Directory>
+    cwd : Directory
         Specify a custom current working directory
 
     Examples
@@ -56,7 +56,7 @@ def sort_ascending(p_files):
 
     Parameters
     ----------
-    p_files <list<string>>
+    p_files : list
         List of image paths to be sorted in ascending order
 
     Examples
@@ -65,8 +65,15 @@ def sort_ascending(p_files):
     >>> sort_ascending(in_files)
 
     """
-    p_files.sort()
-    return p_files
+    from operator import itemgetter
+    import os
+    path_basename = []
+    for f in p_files:
+        path_basename.append((os.path.basename(f), f))
+
+    path_basename = sorted(path_basename, key=itemgetter(0))
+    # p_files.sort()
+    return [f[1] for f in path_basename]
 
 
 def reorder_by_run_ids(p_files, p_order):
diff --git a/pymialsrtk/parser.py b/pymialsrtk/parser.py
index 5a7c44e3a..e9091397d 100644
--- a/pymialsrtk/parser.py
+++ b/pymialsrtk/parser.py
@@ -1,4 +1,4 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
@@ -12,57 +12,90 @@ def get_parser():
     """Create and return the parser object of the BIDS App."""
 
     import argparse
-    p = argparse.ArgumentParser(description='Argument parser of the MIALSRTK BIDS App')
-
-    p.add_argument('bids_dir',
-                   help='The directory with the input dataset '
-                        'formatted according to the BIDS standard.')
-
-    p.add_argument('output_dir',
-                   help='The directory where the output files '
-                        'should be stored. If you are running group level analysis '
-                        'this folder should be prepopulated with the results of the '
-                        'participant level analysis.')
-
-    p.add_argument('analysis_level',
-                   help='Level of the analysis that will be performed. '
-                        'Only participant is available',
-                   choices=['participant'])
-
-    p.add_argument('--participant_label',
-                   help='The label(s) of the participant(s) that should be analyzed. '
-                        'The label corresponds to sub-<participant_label> from the BIDS spec '
-                        '(so it does not include "sub-"). If this parameter is not '
-                        'provided all subjects should be analyzed. Multiple '
-                        'participants can be specified with a space separated list.',
-                   nargs="+")
-
-    p.add_argument('--param_file',
-                   help='Path to a JSON file containing subjects\' exams '
-                        'information and super-resolution total variation parameters.',
-                   default='/bids_dir/code/participants_params.json',
-                   type=str)
-
-    p.add_argument('--openmp_nb_of_cores',
-                   help='Specify number of cores used by OpenMP threads '
-                        'Especially useful for NLM denoising and slice-to-volume registration. '
-                        '(Default: 0, meaning it will be determined automatically)',
-                   default=0,
-                   type=int)
-
-    p.add_argument('--nipype_nb_of_cores',
-                   help='Specify number of cores used by the Niype workflow library to distribute '
-                        'the execution of independent processing workflow nodes (i.e. interfaces) '
-                        '(Especially useful in the case of slice-by-slice bias field correction and '
-                        'intensity standardization steps for example). '
-                        '(Default: 0, meaning it will be determined automatically)',
-                   default=0,
-                   type=int)
-
-    p.add_argument('--masks_derivatives_dir',
-                   help='Use manual brain masks found in '
-                        '``<output_dir>/<masks_derivatives_dir>/ directory`` directory')
-    p.add_argument('-v', '--version',
-                   action='version',
-                   version=f'BIDS-App MIALSRTK version {__version__} (Released: {__release_date__})')
+
+    p = argparse.ArgumentParser(description="Argument parser of the MIALSRTK BIDS App")
+
+    p.add_argument("bids_dir", help="The directory with the input dataset " "formatted according to the BIDS standard.")
+
+    p.add_argument(
+        "output_dir",
+        help="The directory where the output files "
+        "should be stored. If you are running group level analysis "
+        "this folder should be prepopulated with the results of the "
+        "participant level analysis.",
+    )
+
+    p.add_argument(
+        "analysis_level",
+        help="Level of the analysis that will be performed. " "Only participant is available",
+        choices=["participant"],
+    )
+
+    p.add_argument(
+        "--participant_label",
+        help="The label(s) of the participant(s) that should be analyzed. "
+        "The label corresponds to sub-<participant_label> from the BIDS spec "
+        '(so it does not include "sub-"). If this parameter is not '
+        "provided all subjects should be analyzed. Multiple "
+        "participants can be specified with a space separated list.",
+        nargs="+",
+    )
+
+    p.add_argument(
+        "--param_file",
+        help="Path to a JSON file containing subjects' exams "
+        "information and super-resolution total variation parameters.",
+        default="/bids_dir/code/participants_params.json",
+        type=str,
+    )
+
+    p.add_argument(
+        "--openmp_nb_of_cores",
+        help="Specify number of cores used by OpenMP threads "
+        "Especially useful for NLM denoising and slice-to-volume registration. "
+        "(Default: 0, meaning it will be determined automatically)",
+        default=0,
+        type=int,
+    )
+
+    p.add_argument(
+        "--nipype_nb_of_cores",
+        help="Specify number of cores used by the Niype workflow library to distribute "
+        "the execution of independent processing workflow nodes (i.e. interfaces) "
+        "(Especially useful in the case of slice-by-slice bias field correction and "
+        "intensity standardization steps for example). "
+        "(Default: 0, meaning it will be determined automatically)",
+        default=0,
+        type=int,
+    )
+
+    p.add_argument(
+        "--memory",
+        help="Limit the workflow to using the amount of specified memory [in gb] "
+        "(Default: 0, the workflow memory consumption is not limited)",
+        default=0,
+        type=int,
+    )
+
+    p.add_argument(
+        "--masks_derivatives_dir",
+        help="Use manual brain masks found in "
+        "``<output_dir>/<masks_derivatives_dir>/`` directory",
+    )
+
+    p.add_argument(
+        "--profiling",
+        help="Save node runtime statistics (processing times, number of threads, memory) "
+        "in a JSON-style `run_stats.log` and in the form "
+        "of a gantt chart in a HTML `run_stats.log.html`",
+        action="store_true",
+        default=False,
+    )
+
+    p.add_argument(
+        "-v",
+        "--version",
+        action="version",
+        version=f"BIDS-App MIALSRTK version {__version__} (Released: {__release_date__})",
+    )
     return p
diff --git a/pymialsrtk/pipelines/anatomical/srr.py b/pymialsrtk/pipelines/anatomical/srr.py
index c749e46f8..4f5149027 100644
--- a/pymialsrtk/pipelines/anatomical/srr.py
+++ b/pymialsrtk/pipelines/anatomical/srr.py
@@ -1,25 +1,35 @@
-# Copyright © 2016-2020 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
 #
 #  This software is distributed under the open-source license Modified BSD.
 
 """Module for the super-resolution reconstruction pipeline."""
 
 import os
-
+import sys
+import platform
+import json
+import shutil
 import pkg_resources
+from datetime import datetime
+
+from jinja2 import Environment, FileSystemLoader
+from jinja2 import __version__ as __jinja2_version__
+
+import nibabel as nib
 
+from nipype.info import __version__ as __nipype_version__
 from nipype import config, logging
-# from nipype.interfaces.io import BIDSDataGrabber
 from nipype.interfaces.io import DataGrabber, DataSink
 from nipype.pipeline import Node, MapNode, Workflow
 from nipype.interfaces.utility import IdentityInterface, Function
 
-
 # Import the implemented interface from pymialsrtk
 import pymialsrtk.interfaces.preprocess as preprocess
 import pymialsrtk.interfaces.reconstruction as reconstruction
 import pymialsrtk.interfaces.postprocess as postprocess
 import pymialsrtk.interfaces.utils as utils
+from pymialsrtk.bids.utils import write_bids_derivative_description
+from pymialsrtk.utils.monitoring import log_nodes_cb, generate_gantt_chart
 
 # Get pymialsrtk version
 from pymialsrtk.info import __version__
@@ -30,72 +40,71 @@ class AnatomicalPipeline:
 
     Attributes
     -----------
-    bids_dir <string>
+    bids_dir : string
         BIDS root directory (required)
 
-    output_dir <string>
+    output_dir : string
         Output derivatives directory (required)
 
-    subject <string>
+    subject : string
         Subject ID (in the form ``sub-XX``)
 
-    wf <nipype.pipeline.Workflow>
+    wf : nipype.pipeline.Workflow
         Nipype workflow of the reconstruction pipeline
 
-    dictsink <nipype.interfaces.io.JSONFileSink>
-        Nipype node used to generate a JSON file that store provenance metadata
-        for the SR-reconstructed images
-
-    deltatTV <string>
+    deltatTV : string
         Super-resolution optimization time-step
 
-    lambdaTV <Float>
+    lambdaTV : float
         Regularization weight (default is 0.75)
 
-    primal_dual_loops <string>
+    primal_dual_loops : string
         Number of primal/dual loops used in the optimization of the total-variation
         super-resolution algorithm.
 
-    sr_id <string>
+    sr_id : string
         ID of the reconstruction useful to distinguish when multiple reconstructions
         with different order of stacks are run on the same subject
 
-    session <string>
+    session : string
         Session ID if applicable (in the form ``ses-YY``)
 
-    m_stacks list<<int>>
-        List of stack to be used in the reconstruction. The specified order is kept if `skip_stacks_ordering` is True.
+    m_stacks : list(int)
+        List of stack to be used in the reconstruction.
+        The specified order is kept if `skip_stacks_ordering` is True.
 
-    m_masks_derivatives_dir <string>
+    m_masks_derivatives_dir : string
         directory basename in BIDS directory derivatives where to search for masks (optional)
 
-    m_skip_svr <bool>
-        Weither the Slice-to-Volume Registration should be skipped in the image reconstruction. (default is False)
+    m_skip_svr : bool
+        Weither the Slice-to-Volume Registration should be skipped in the image reconstruction.
+        (default is False)
 
-    m_do_refine_hr_mask <bool>
+    m_do_refine_hr_mask : bool
         Weither a refinement of the HR mask should be performed. (default is False)
 
-    m_skip_nlm_denoising <bool>
+    m_skip_nlm_denoising : bool
         Weither the NLM denoising preprocessing should be skipped. (default is False)
 
-    m_skip_stacks_ordering <bool> (optional)
+    m_skip_stacks_ordering : bool (optional)
         Weither the automatic stacks ordering should be skipped. (default is False)
 
-
     Examples
     --------
     >>> from pymialsrtk.pipelines.anatomical.srr import AnatomicalPipeline
     >>> # Create a new instance
-    >>> pipeline = AnatomicalPipeline('/path/to/bids_dir',
-                                  '/path/to/output_dir',
-                                  'sub-01',
-                                  [1,3,2,0],
-                                  01,
-                                  None,
-                                  paramTV={deltatTV = "0.001",
-                                           lambdaTV = "0.75",
-                                           primal_dual_loops = "20"},
-                                  use_manual_masks=False)
+    >>> pipeline = AnatomicalPipeline(bids_dir='/path/to/bids_dir',
+                                      output_dir='/path/to/output_dir',
+                                      subject='sub-01',
+                                      p_stacks=[1,3,2,0],
+                                      sr_id=1,
+                                      session=None,
+                                      paramTV={deltatTV = "0.001",
+                                               lambdaTV = "0.75",
+                                               primal_dual_loops = "20"},
+                                      masks_derivatives_dir="/custom/mask_dir",
+                                      masks_desc=None,
+                                      p_dict_custom_interfaces=None)
     >>> # Create the super resolution Nipype workflow
     >>> pipeline.create_workflow()
     >>> # Execute the workflow
@@ -103,6 +112,11 @@ class AnatomicalPipeline:
 
     """
 
+    pipeline_name = "srr_pipeline"
+    run_start_time = None
+    run_end_time = None
+    run_elapsed_time = None
+
     bids_dir = None
     output_dir = None
     subject = None
@@ -123,10 +137,17 @@ class AnatomicalPipeline:
 
     m_masks_derivatives_dir = None
     use_manual_masks = False
+    m_masks_desc = None
+
+    openmp_number_of_cores = None
+    nipype_number_of_cores = None
 
-    def __init__(self, bids_dir, output_dir, subject, p_stacks=None, sr_id=1,
-                 session=None, paramTV=None, p_masks_derivatives_dir=None,
-                 p_dict_custom_interfaces = None):
+    def __init__(
+        self, bids_dir, output_dir, subject, p_stacks=None, sr_id=1,
+        session=None, paramTV=None, p_masks_derivatives_dir=None, p_masks_desc=None,
+        p_dict_custom_interfaces=None,
+        openmp_number_of_cores=None, nipype_number_of_cores=None,
+    ):
         """Constructor of AnatomicalPipeline class instance."""
 
         # BIDS processing parameters
@@ -137,6 +158,9 @@ def __init__(self, bids_dir, output_dir, subject, p_stacks=None, sr_id=1,
         self.session = session
         self.m_stacks = p_stacks
 
+        self.openmp_number_of_cores = openmp_number_of_cores
+        self.nipype_number_of_cores = nipype_number_of_cores
+
         # (default) sr tv parameters
         if paramTV is None:
             paramTV = dict()
@@ -148,12 +172,13 @@ def __init__(self, bids_dir, output_dir, subject, p_stacks=None, sr_id=1,
         # If masks directory is not specified use the automated brain extraction method.
         self.m_masks_derivatives_dir = p_masks_derivatives_dir
         self.use_manual_masks = True if self.m_masks_derivatives_dir is not None else False
+        self.m_masks_desc = p_masks_desc if self.use_manual_masks else None
 
         # Custom interfaces and default values.
         if p_dict_custom_interfaces is not None:
-            self.m_skip_svr = p_dict_custom_interfaces['skip_svr'] if 'skip_svr' in  p_dict_custom_interfaces.keys() else False
-            self.m_do_refine_hr_mask = p_dict_custom_interfaces['do_refine_hr_mask'] if 'do_refine_hr_mask' in  p_dict_custom_interfaces.keys() else False
-            self.m_skip_nlm_denoising = p_dict_custom_interfaces['skip_nlm_denoising'] if 'skip_nlm_denoising' in  p_dict_custom_interfaces.keys() else False
+            self.m_skip_svr = p_dict_custom_interfaces['skip_svr'] if 'skip_svr' in p_dict_custom_interfaces.keys() else False
+            self.m_do_refine_hr_mask = p_dict_custom_interfaces['do_refine_hr_mask'] if 'do_refine_hr_mask' in p_dict_custom_interfaces.keys() else False
+            self.m_skip_nlm_denoising = p_dict_custom_interfaces['skip_nlm_denoising'] if 'skip_nlm_denoising' in p_dict_custom_interfaces.keys() else False
 
             self.m_skip_stacks_ordering = p_dict_custom_interfaces['skip_stacks_ordering'] if \
                 ((self.m_stacks is not None) and ('skip_stacks_ordering' in p_dict_custom_interfaces.keys())) else False
@@ -163,12 +188,17 @@ def __init__(self, bids_dir, output_dir, subject, p_stacks=None, sr_id=1,
             self.m_skip_nlm_denoising =  False
             self.m_skip_stacks_ordering = False
 
-    def create_workflow(self):
+    def create_workflow(self, save_profiler_log=False):
         """Create the Niype workflow of the super-resolution pipeline.
 
         It is composed of a succession of Nodes and their corresponding parameters,
         where the output of node i goes to the input of node i+1.
 
+        Parameters
+        ----------
+        save_profiler_log : bool
+            If `True`, save node runtime statistics to a JSON-style log file.
+
         """
 
         sub_ses = self.subject
@@ -177,7 +207,7 @@ def create_workflow(self):
 
         if self.session is None:
             wf_base_dir = os.path.join(self.output_dir,
-                                       "nipype",
+                                       '-'.join(["nipype", __nipype_version__]),
                                        self.subject,
                                        "rec-{}".format(self.sr_id))
             final_res_dir = os.path.join(self.output_dir,
@@ -185,7 +215,7 @@ def create_workflow(self):
                                          self.subject)
         else:
             wf_base_dir = os.path.join(self.output_dir,
-                                       "nipype",
+                                       '-'.join(["nipype", __nipype_version__]),
                                        self.subject,
                                        self.session,
                                        "rec-{}".format(self.sr_id))
@@ -194,73 +224,103 @@ def create_workflow(self):
                                          self.subject,
                                          self.session)
 
-        # #if self.sr_id is not None:
-        # wf_base_dir = os.path.join(wf_base_dir, self.sr_id)
-
         if not os.path.exists(wf_base_dir):
             os.makedirs(wf_base_dir)
         print("Process directory: {}".format(wf_base_dir))
 
-        # Workflow name cannot begin with a number (oterhwise ValueError)
-        pipeline_name = "srr_pipeline"
-
-        self.wf = Workflow(name=pipeline_name,base_dir=wf_base_dir)
-        # srr_nipype_dir = os.path.join(self.wf.base_dir, self.wf.name )
-
         # Initialization (Not sure we can control the name of nipype log)
-        if os.path.isfile(os.path.join(wf_base_dir, "pypeline_" + sub_ses + ".log")):
-            os.unlink(os.path.join(wf_base_dir, "pypeline_" + sub_ses + ".log"))
-            # open(os.path.join(self.output_dir,"pypeline.log"), 'a').close()
-
-        config.update_config({'logging': {'log_directory': os.path.join(wf_base_dir),
-                                          'log_to_file': True},
-                              'execution': {
-                                  'remove_unnecessary_outputs': False,
-                                  'stop_on_first_crash': True,
-                                  'stop_on_first_rerun': False,
-                                  'crashfile_format': "txt",
-                                  'write_provenance': False},
-                              'monitoring': {'enabled': True}
-                              })
-
-        # config.enable_provenance()
-
+        if os.path.isfile(os.path.join(wf_base_dir, "pypeline.log")):
+            os.unlink(os.path.join(wf_base_dir, "pypeline.log"))
+
+        if save_profiler_log:
+            log_filename = os.path.join(wf_base_dir, 'pypeline_stats.log')
+            if os.path.isfile(log_filename):
+                os.unlink(log_filename)
+
+        self.wf = Workflow(name=self.pipeline_name,base_dir=wf_base_dir)
+
+        config.update_config(
+            {
+                'logging': {
+                      'log_directory': os.path.join(wf_base_dir),
+                      'log_to_file': True
+                },
+                'execution': {
+                    'remove_unnecessary_outputs': False,
+                    'stop_on_first_crash': True,
+                    'stop_on_first_rerun': False,
+                    'crashfile_format': "txt",
+                    'use_relative_paths': True,
+                    'write_provenance': False
+                }
+            }
+        )
+
+        if save_profiler_log:
+            config.update_config(
+                {
+                    'monitoring': {
+                        'enabled': save_profiler_log,
+                        'sample_frequency': "1.0",
+                        'summary_append': True
+                    }
+                }
+            )
+            config.enable_resource_monitor()
+
+        # Update logging with config
         logging.update_logging(config)
-        iflogger = logging.getLogger('nipype.interface')
-
-        iflogger.info("**** Processing ****")
+        # config.enable_provenance()
 
         if self.use_manual_masks:
-            dg = Node(interface=DataGrabber(outfields=['T2ws', 'masks']), name='data_grabber')
-
+            dg = Node(
+                interface=DataGrabber(outfields=['T2ws', 'masks']),
+                name='data_grabber'
+            )
             dg.inputs.base_directory = self.bids_dir
             dg.inputs.template = '*'
             dg.inputs.raise_on_empty = False
             dg.inputs.sort_filelist = True
 
-            dg.inputs.field_template = dict(T2ws=os.path.join(self.subject, 'anat', sub_ses+'*_run-*_T2w.nii.gz'),
-                                            masks=os.path.join('derivatives',
-                                                               self.m_masks_derivatives_dir,
-                                                               self.subject,
-                                                               'anat',
-                                                               sub_ses+'*_run-*_*mask.nii.gz'))
             if self.session is not None:
-                dg.inputs.field_template = dict(T2ws=os.path.join(self.subject,
-                                                                  self.session,
-                                                                  'anat',
-                                                                  '_'.join([sub_ses, '*run-*', '*T2w.nii.gz'])),
-                                                masks=os.path.join('derivatives',
-                                                                   self.m_masks_derivatives_dir,
-                                                                   self.subject,
-                                                                   self.session,
-                                                                   'anat',
-                                                                   '_'.join([sub_ses, '*run-*', '*mask.nii.gz'])))
+                t2ws_template = os.path.join(
+                    self.subject, self.session, 'anat',
+                    '_'.join([sub_ses, '*run-*', '*T2w.nii.gz'])
+                )
+                if self.m_masks_desc is not None:
+                    masks_template = os.path.join(
+                        'derivatives', self.m_masks_derivatives_dir, self.subject, self.session, 'anat',
+                        '_'.join([sub_ses, '*_run-*', '_desc-'+self.m_masks_desc, '*mask.nii.gz'])
+                    )
+                else:
+                    masks_template = os.path.join(
+                        'derivatives', self.m_masks_derivatives_dir, self.subject, self.session, 'anat',
+                        '_'.join([sub_ses, '*run-*', '*mask.nii.gz'])
+                    )
+            else:
+                t2ws_template=os.path.join(self.subject, 'anat', sub_ses + '*_run-*_T2w.nii.gz')
+
+                if self.m_masks_desc is not None:
+                    masks_template = os.path.join(
+                        'derivatives', self.m_masks_derivatives_dir, self.subject, self.session, 'anat',
+                        '_'.join([sub_ses, '*_run-*', '_desc-'+self.m_masks_desc, '*mask.nii.gz'])
+                    )
+                else:
+                    masks_template = os.path.join(
+                        'derivatives', self.m_masks_derivatives_dir, self.subject, 'anat',
+                        sub_ses + '*_run-*_*mask.nii.gz'
+                    )
+
+            dg.inputs.field_template = dict(T2ws=t2ws_template,
+                                            masks=masks_template)
+
             brainMask = MapNode(interface=IdentityInterface(fields=['out_file']),
                                 name='brain_masks_bypass',
                                 iterfield=['out_file'])
 
         else:
-            dg = Node(interface=DataGrabber(outfields=['T2ws']), name='data_grabber')
+            dg = Node(interface=DataGrabber(outfields=['T2ws']),
+                      name='data_grabber')
 
             dg.inputs.base_directory = self.bids_dir
             dg.inputs.template = '*'
@@ -268,14 +328,15 @@ def create_workflow(self):
             dg.inputs.sort_filelist = True
 
             dg.inputs.field_template = dict(T2ws=os.path.join(self.subject,
-                                                              'anat', sub_ses+'*_run-*_T2w.nii.gz'))
+                                                              'anat',
+                                                              sub_ses+'*_run-*_T2w.nii.gz'))
             if self.session is not None:
                 dg.inputs.field_template = dict(T2ws=os.path.join(self.subject,
                                                                   self.session, 'anat', '_'.join([sub_ses, '*run-*', '*T2w.nii.gz'])))
 
             if self.m_stacks is not None:
-                print('if is not self.m_stacks !!! ')
-                t2ws_filter_prior_masks = Node(interface=preprocess.FilteringByRunid(), name='t2ws_filter_prior_masks')
+                t2ws_filter_prior_masks = Node(interface=preprocess.FilteringByRunid(),
+                                               name='t2ws_filter_prior_masks')
                 t2ws_filter_prior_masks.inputs.stacks_id = self.m_stacks
 
             brainMask = MapNode(interface = preprocess.BrainExtraction(),
@@ -283,27 +344,34 @@ def create_workflow(self):
                                 iterfield=['in_file'])
 
             brainMask.inputs.bids_dir = self.bids_dir
-            brainMask.inputs.in_ckpt_loc = pkg_resources.resource_filename("pymialsrtk",
-                                                                           os.path.join("data",
-                                                                                        "Network_checkpoints",
-                                                                                        "Network_checkpoints_localization",
-                                                                                        "Unet.ckpt-88000.index")).split('.index')[0]
+            brainMask.inputs.in_ckpt_loc = pkg_resources.resource_filename(
+                "pymialsrtk",
+                os.path.join("data",
+                             "Network_checkpoints",
+                             "Network_checkpoints_localization",
+                             "Unet.ckpt-88000.index")
+            ).split('.index')[0]
             brainMask.inputs.threshold_loc = 0.49
-            brainMask.inputs.in_ckpt_seg = pkg_resources.resource_filename("pymialsrtk",
-                                                                           os.path.join("data",
-                                                                                        "Network_checkpoints",
-                                                                                        "Network_checkpoints_segmentation",
-                                                                                        "Unet.ckpt-20000.index")).split('.index')[0]
+            brainMask.inputs.in_ckpt_seg = pkg_resources.resource_filename(
+                "pymialsrtk",
+                os.path.join("data",
+                             "Network_checkpoints",
+                             "Network_checkpoints_segmentation",
+                             "Unet.ckpt-20000.index")
+            ).split('.index')[0]
             brainMask.inputs.threshold_seg = 0.5
 
-        t2ws_filtered = Node(interface=preprocess.FilteringByRunid(), name='t2ws_filtered')
-        masks_filtered = Node(interface=preprocess.FilteringByRunid(), name='masks_filtered')
-
+        t2ws_filtered = Node(interface=preprocess.FilteringByRunid(),
+                             name='t2ws_filtered')
+        masks_filtered = Node(interface=preprocess.FilteringByRunid(),
+                              name='masks_filtered')
 
         if not self.m_skip_stacks_ordering:
-            stacksOrdering = Node(interface=preprocess.StacksOrdering(), name='stackOrdering')
+            stacksOrdering = Node(interface=preprocess.StacksOrdering(),
+                                  name='stackOrdering')
         else:
-            stacksOrdering = Node(interface=IdentityInterface(fields=['stacks_order']), name='stackOrdering')
+            stacksOrdering = Node(interface=IdentityInterface(fields=['stacks_order']),
+                                  name='stackOrdering')
             stacksOrdering.inputs.stacks_order = self.m_stacks
 
         if not self.m_skip_nlm_denoising:
@@ -321,12 +389,12 @@ def create_workflow(self):
 
         srtkCorrectSliceIntensity01 = MapNode(interface=preprocess.MialsrtkCorrectSliceIntensity(),
                                               name='srtkCorrectSliceIntensity01',
-                                                  iterfield=['in_file', 'in_mask'])
+                                              iterfield=['in_file', 'in_mask'])
         srtkCorrectSliceIntensity01.inputs.bids_dir = self.bids_dir
         srtkCorrectSliceIntensity01.inputs.out_postfix = '_uni'
 
         srtkSliceBySliceN4BiasFieldCorrection = MapNode(interface=preprocess.MialsrtkSliceBySliceN4BiasFieldCorrection(),
-                                                     name='srtkSliceBySliceN4BiasFieldCorrection',
+                                                        name='srtkSliceBySliceN4BiasFieldCorrection',
                                                         iterfield=['in_file', 'in_mask'])
         srtkSliceBySliceN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
 
@@ -335,51 +403,56 @@ def create_workflow(self):
                                                    iterfield=['in_file', 'in_mask', 'in_field'])
         srtkSliceBySliceCorrectBiasField.inputs.bids_dir = self.bids_dir
 
-
-    # 4-modules sequence to be defined as a stage.
+        # 4-modules sequence to be defined as a stage.
         if not self.m_skip_nlm_denoising:
             srtkCorrectSliceIntensity02_nlm = MapNode(interface=preprocess.MialsrtkCorrectSliceIntensity(),
                                                       name='srtkCorrectSliceIntensity02_nlm',
-                                                      iterfield=['in_file','in_mask'])
+                                                      iterfield=['in_file', 'in_mask'])
             srtkCorrectSliceIntensity02_nlm.inputs.bids_dir = self.bids_dir
 
-            srtkIntensityStandardization01_nlm = Node(interface=preprocess.MialsrtkIntensityStandardization(), name='srtkIntensityStandardization01_nlm')
+            srtkIntensityStandardization01_nlm = Node(interface=preprocess.MialsrtkIntensityStandardization(),
+                                                      name='srtkIntensityStandardization01_nlm')
             srtkIntensityStandardization01_nlm.inputs.bids_dir = self.bids_dir
 
-            srtkHistogramNormalization_nlm = Node(interface=preprocess.MialsrtkHistogramNormalization(), name='srtkHistogramNormalization_nlm')
+            srtkHistogramNormalization_nlm = Node(interface=preprocess.MialsrtkHistogramNormalization(),
+                                                  name='srtkHistogramNormalization_nlm')
             srtkHistogramNormalization_nlm.inputs.bids_dir = self.bids_dir
 
-            srtkIntensityStandardization02_nlm = Node(interface=preprocess.MialsrtkIntensityStandardization(), name='srtkIntensityStandardization02_nlm')
+            srtkIntensityStandardization02_nlm = Node(interface=preprocess.MialsrtkIntensityStandardization(),
+                                                      name='srtkIntensityStandardization02_nlm')
             srtkIntensityStandardization02_nlm.inputs.bids_dir = self.bids_dir
 
-
-    # 4-modules sequence to be defined as a stage.
+        # 4-modules sequence to be defined as a stage.
         srtkCorrectSliceIntensity02 = MapNode(interface=preprocess.MialsrtkCorrectSliceIntensity(),
-                                          name='srtkCorrectSliceIntensity02',
-                                          iterfield=['in_file', 'in_mask'])
+                                              name='srtkCorrectSliceIntensity02',
+                                              iterfield=['in_file', 'in_mask'])
         srtkCorrectSliceIntensity02.inputs.bids_dir = self.bids_dir
 
-        srtkIntensityStandardization01 = Node(interface=preprocess.MialsrtkIntensityStandardization(), name='srtkIntensityStandardization01')
+        srtkIntensityStandardization01 = Node(interface=preprocess.MialsrtkIntensityStandardization(),
+                                              name='srtkIntensityStandardization01')
         srtkIntensityStandardization01.inputs.bids_dir = self.bids_dir
 
-        srtkHistogramNormalization = Node(interface=preprocess.MialsrtkHistogramNormalization(), name='srtkHistogramNormalization')
+        srtkHistogramNormalization = Node(interface=preprocess.MialsrtkHistogramNormalization(),
+                                          name='srtkHistogramNormalization')
         srtkHistogramNormalization.inputs.bids_dir = self.bids_dir
 
-        srtkIntensityStandardization02 = Node(interface=preprocess.MialsrtkIntensityStandardization(), name='srtkIntensityStandardization02')
+        srtkIntensityStandardization02 = Node(interface=preprocess.MialsrtkIntensityStandardization(),
+                                              name='srtkIntensityStandardization02')
         srtkIntensityStandardization02.inputs.bids_dir = self.bids_dir
 
-
         srtkMaskImage01 = MapNode(interface=preprocess.MialsrtkMaskImage(),
                                   name='srtkMaskImage01',
                                   iterfield=['in_file', 'in_mask'])
         srtkMaskImage01.inputs.bids_dir = self.bids_dir
 
-        srtkImageReconstruction = Node(interface=reconstruction.MialsrtkImageReconstruction(), name='srtkImageReconstruction')
+        srtkImageReconstruction = Node(interface=reconstruction.MialsrtkImageReconstruction(),
+                                       name='srtkImageReconstruction')
         srtkImageReconstruction.inputs.bids_dir = self.bids_dir
         srtkImageReconstruction.inputs.sub_ses = sub_ses
         srtkImageReconstruction.inputs.no_reg = self.m_skip_svr
 
-        srtkTVSuperResolution = Node(interface=reconstruction.MialsrtkTVSuperResolution(), name='srtkTVSuperResolution')
+        srtkTVSuperResolution = Node(interface=reconstruction.MialsrtkTVSuperResolution(),
+                                     name='srtkTVSuperResolution')
         srtkTVSuperResolution.inputs.bids_dir = self.bids_dir
         srtkTVSuperResolution.inputs.sub_ses = sub_ses
         srtkTVSuperResolution.inputs.in_loop = self.primal_dual_loops
@@ -387,31 +460,28 @@ def create_workflow(self):
         srtkTVSuperResolution.inputs.in_lambda = self.lambdaTV
         srtkTVSuperResolution.inputs.use_manual_masks = self.use_manual_masks
 
-        srtkN4BiasFieldCorrection = Node(interface=postprocess.MialsrtkN4BiasFieldCorrection(), name='srtkN4BiasFieldCorrection')
+        srtkN4BiasFieldCorrection = Node(interface=postprocess.MialsrtkN4BiasFieldCorrection(),
+                                         name='srtkN4BiasFieldCorrection')
         srtkN4BiasFieldCorrection.inputs.bids_dir = self.bids_dir
 
         if self.m_do_refine_hr_mask:
-            srtkHRMask = Node(interface=postprocess.MialsrtkRefineHRMaskByIntersection(), name='srtkHRMask')
+            srtkHRMask = Node(interface=postprocess.MialsrtkRefineHRMaskByIntersection(),
+                              name='srtkHRMask')
             srtkHRMask.inputs.bids_dir = self.bids_dir
         else:
-            srtkHRMask = Node(interface=Function(input_names=["input_image"], output_names=["output_srmask"],
-                                    function=postprocess.binarize_image), name='srtkHRMask')
-
-        srtkMaskImage02 = Node(interface=preprocess.MialsrtkMaskImage(), name='srtkMaskImage02')
+            srtkHRMask = Node(interface=Function(
+                              input_names=["input_image"],
+                              output_names=["output_srmask"],
+                              function=postprocess.binarize_image
+                              ),
+                              name='srtkHRMask')
+
+        srtkMaskImage02 = Node(interface=preprocess.MialsrtkMaskImage(),
+                               name='srtkMaskImage02')
         srtkMaskImage02.inputs.bids_dir = self.bids_dir
 
-        finalFilenamesGeneration = Node(postprocess.FilenamesGeneration(), name='filenames_gen')
-        finalFilenamesGeneration.inputs.sub_ses = sub_ses
-        finalFilenamesGeneration.inputs.sr_id = self.sr_id
-        finalFilenamesGeneration.inputs.use_manual_masks = self.use_manual_masks
-
-        datasink = Node(DataSink(), name='data_sinker')
-        datasink.inputs.base_directory = final_res_dir
-
-
-        # - Build workflow : connections of the nodes
-
-        # Nodes ready - Linking now
+        # Build workflow : connections of the nodes
+        # Nodes ready : Linking now
         if self.use_manual_masks:
             self.wf.connect(dg, "masks", brainMask, "out_file")
         else:
@@ -461,12 +531,10 @@ def create_workflow(self):
         self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), srtkCorrectSliceIntensity02, "in_mask")
         self.wf.connect(srtkCorrectSliceIntensity02, ("out_file", utils.sort_ascending), srtkIntensityStandardization01, "input_images")
 
-
         self.wf.connect(srtkIntensityStandardization01, ("output_images", utils.sort_ascending), srtkHistogramNormalization, "input_images")
         self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), srtkHistogramNormalization, "input_masks")
         self.wf.connect(srtkHistogramNormalization, ("output_images", utils.sort_ascending), srtkIntensityStandardization02, "input_images")
 
-
         if not self.m_skip_nlm_denoising:
             self.wf.connect(srtkIntensityStandardization02_nlm, ("output_images", utils.sort_ascending), srtkMaskImage01, "in_file")
             self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), srtkMaskImage01, "in_mask")
@@ -485,7 +553,6 @@ def create_workflow(self):
 
         self.wf.connect(srtkImageReconstruction, "output_sdi", srtkTVSuperResolution, "input_sdi")
 
-
         if self.m_do_refine_hr_mask:
             self.wf.connect(srtkIntensityStandardization02, ("output_images", utils.sort_ascending), srtkHRMask, "input_images")
             self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), srtkHRMask, "input_masks")
@@ -497,24 +564,36 @@ def create_workflow(self):
         self.wf.connect(srtkTVSuperResolution, "output_sr", srtkMaskImage02, "in_file")
         self.wf.connect(srtkHRMask, "output_srmask", srtkMaskImage02, "in_mask")
 
-
         self.wf.connect(srtkTVSuperResolution, "output_sr", srtkN4BiasFieldCorrection, "input_image")
         self.wf.connect(srtkHRMask, "output_srmask", srtkN4BiasFieldCorrection, "input_mask")
 
+        # Datasinker
+        finalFilenamesGeneration = Node(interface=postprocess.FilenamesGeneration(),
+                                        name='filenames_gen')
+        finalFilenamesGeneration.inputs.sub_ses = sub_ses
+        finalFilenamesGeneration.inputs.sr_id = self.sr_id
+        finalFilenamesGeneration.inputs.use_manual_masks = self.use_manual_masks
+
         self.wf.connect(stacksOrdering, "stacks_order", finalFilenamesGeneration, "stacks_order")
-        self.wf.connect(finalFilenamesGeneration, "substitutions", datasink, "substitutions")
-        self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), datasink, 'anat.@LRmasks')
 
+        datasink = Node(interface=DataSink(), name='data_sinker')
+        datasink.inputs.base_directory = final_res_dir
+
+        if not self.m_skip_stacks_ordering:
+            self.wf.connect(stacksOrdering, "report_image", datasink, 'figures.@stackOrderingQC')
+            self.wf.connect(stacksOrdering, "motion_tsv", datasink, 'anat.@motionTSV')
+        self.wf.connect(masks_filtered, ("output_files", utils.sort_ascending), datasink, 'anat.@LRmasks')
         self.wf.connect(srtkIntensityStandardization02, ("output_images", utils.sort_ascending), datasink, 'anat.@LRsPreproc')
-        self.wf.connect(srtkMaskImage01, ("out_im_file", utils.sort_ascending), datasink, 'anat.@LRsDenoised')
         self.wf.connect(srtkImageReconstruction, ("output_transforms", utils.sort_ascending), datasink, 'xfm.@transforms')
-
+        self.wf.connect(finalFilenamesGeneration, "substitutions", datasink, "substitutions")
+        self.wf.connect(srtkMaskImage01, ("out_im_file", utils.sort_ascending), datasink, 'anat.@LRsDenoised')
         self.wf.connect(srtkImageReconstruction, "output_sdi", datasink, 'anat.@SDI')
         self.wf.connect(srtkN4BiasFieldCorrection, "output_image", datasink, 'anat.@SR')
         self.wf.connect(srtkTVSuperResolution, "output_json_path", datasink, 'anat.@SRjson')
+        self.wf.connect(srtkTVSuperResolution, "output_sr_png", datasink, 'figures.@SRpng')
         self.wf.connect(srtkHRMask, "output_srmask", datasink, 'anat.@SRmask')
 
-    def run(self, number_of_cores=1):
+    def run(self, number_of_cores=1, memory=None, save_profiler_log=False):
         """Execute the workflow of the super-resolution reconstruction pipeline.
 
         Nipype execution engine will take care of the management and execution of
@@ -524,16 +603,276 @@ def run(self, number_of_cores=1):
 
         Parameters
         ----------
-        number_of_cores <int>
+        number_of_cores : int
             Number of cores / CPUs used by the workflow
 
+        memory : int
+            Maximal memory used by the workflow
+
+        save_profiler_log : bool
+            If `True`, generates the profiling callback log
+            (Default: `False`)
         """
+        from nipype import logging as nipype_logging
 
+        # Use nipype.interface logger to print some information messages
+        iflogger = nipype_logging.getLogger('nipype.interface')
+        iflogger.info("**** Workflow graph creation ****")
         self.wf.write_graph(dotfilename='graph.dot', graph2use='colored', format='png', simple_form=True)
-        if number_of_cores > 1:
-            res = self.wf.run(plugin='MultiProc', plugin_args={'n_procs': number_of_cores})
 
+        # Copy and rename the generated "graph.png" image
+        src = os.path.join(self.wf.base_dir, self.wf.name, 'graph.png')
+        if self.session is not None:
+            dst = os.path.join(
+                self.output_dir,
+                '-'.join(["pymialsrtk", __version__]),
+                self.subject,
+                self.session,
+                'figures',
+                f'{self.subject}_{self.session}_rec-SR_id-{self.sr_id}_desc-processing_graph.png'
+            )
+        else:
+            dst = os.path.join(
+                    self.output_dir,
+                    '-'.join(["pymialsrtk", __version__]),
+                    self.subject,
+                    'figures',
+                    f'{self.subject}_rec-SR_id-{self.sr_id}_desc-processing_graph.png'
+            )
+        # Create the figures/ and parent directories if they do not exist
+        figures_dir = os.path.dirname(dst)
+        os.makedirs(figures_dir, exist_ok=True)
+        # Make the copy
+        iflogger.info(f'\t > Copy {src} to {dst}...')
+        shutil.copy(src=src, dst=dst)
+
+        # Create dictionary of arguments passed to plugin_args
+        args_dict = {
+            'maxtasksperchild': 1,
+            'raise_insufficient': False,
+            'n_procs': number_of_cores
+        }
+
+        if (memory is not None) and (memory > 0):
+            args_dict['memory_gb'] = memory
+
+        if save_profiler_log:
+            args_dict['status_callback'] = log_nodes_cb
+            # Set path to log file and create callback logger
+            callback_log_path = os.path.join(self.wf.base_dir,
+                                             self.wf.name,
+                                             'run_stats.log')
+            import logging
+            import logging.handlers
+            logger = logging.getLogger('callback')
+            logger.setLevel(logging.DEBUG)
+            handler = logging.FileHandler(callback_log_path)
+            logger.addHandler(handler)
+
+        iflogger.info("**** Processing ****")
+        # datetime object containing current start date and time
+        start = datetime.now()
+        self.run_start_time = start.strftime("%B %d, %Y / %H:%M:%S")
+        print(f" Start date / time : {self.run_start_time}")
+
+        # Execute the workflow
+        res = self.wf.run(plugin='MultiProc', plugin_args=args_dict)
+
+        # Copy and rename the workflow execution log
+        src = os.path.join(self.wf.base_dir, "pypeline.log")
+        if self.session is not None:
+            dst = os.path.join(
+                    self.output_dir,
+                    '-'.join(["pymialsrtk", __version__]),
+                    self.subject,
+                    self.session,
+                    'logs',
+                    f'{self.subject}_{self.session}_rec-SR_id-{self.sr_id}_log.txt'
+            )
         else:
-            res = self.wf.run()
+            dst = os.path.join(
+                    self.output_dir,
+                    '-'.join(["pymialsrtk", __version__]),
+                    self.subject,
+                    'logs',
+                    f'{self.subject}_rec-SR_id-{self.sr_id}_log.txt'
+            )
+        # Create the logs/ and parent directories if they do not exist
+        logs_dir = os.path.dirname(dst)
+        os.makedirs(logs_dir, exist_ok=True)
+        # Make the copy
+        iflogger.info(f'\t > Copy {src} to {dst}...')
+        shutil.copy(src=src, dst=dst)
+
+        # datetime object containing current end date and time
+        end = datetime.now()
+        self.run_end_time = end.strftime("%B %d, %Y / %H:%M:%S")
+        print(f" End date / time : {self.run_end_time}")
+
+        # Compute elapsed running time in minutes and seconds
+        duration = end - start
+        (minutes, seconds) = divmod(duration.total_seconds(), 60)
+        self.run_elapsed_time = f'{int(minutes)} minutes and {int(seconds)} seconds'
+        print(f" Elapsed time: {self.run_end_time}")
+
+        iflogger.info("**** Write dataset derivatives description ****")
+        for toolbox in ["pymialsrtk", "nipype"]:
+            write_bids_derivative_description(
+                bids_dir=self.bids_dir,
+                deriv_dir=self.output_dir,
+                pipeline_name=toolbox
+            )
+
+        if save_profiler_log:
+            iflogger.info("**** Workflow execution profiling ****")
+            iflogger.info(f'\t > Creation of report...')
+            generate_gantt_chart(logfile=callback_log_path,
+                                 cores=number_of_cores,
+                                 minute_scale=10,
+                                 space_between_minutes=50,
+                                 pipeline_name=os.path.join(self.wf.base_dir,
+                                                            self.wf.name))
+            # Copy and rename the computational resources log
+            src = os.path.join(self.wf.base_dir, self.wf.name, "run_stats.log.html")
+            if self.session is not None:
+                dst = os.path.join(
+                        self.output_dir,
+                        '-'.join(["pymialsrtk", __version__]),
+                        self.subject,
+                        self.session,
+                        'logs',
+                        f'{self.subject}_{self.session}_rec-SR_id-{self.sr_id}_desc-profiling_log.html'
+                )
+            else:
+                dst = os.path.join(
+                        self.output_dir,
+                        '-'.join(["pymialsrtk", __version__]),
+                        self.subject,
+                        'logs',
+                        f'{self.subject}_rec-SR_id-{self.sr_id}_desc-profiling_log.html'
+                )
+            # Make the copy
+            iflogger.info(f'\t > Copy {src} to {dst}...')
+            shutil.copy(src=src, dst=dst)
+
+        iflogger.info("**** Super-resolution HTML report creation ****")
+        self.create_subject_report()
 
         return res
+
+    def create_subject_report(self):
+        """Create the HTML report"""
+        # Set main subject derivatives directory
+        if self.session is None:
+            sub_ses = self.subject
+            final_res_dir = os.path.join(self.output_dir,
+                                         '-'.join(["pymialsrtk", __version__]),
+                                         self.subject)
+        else:
+            sub_ses = f'{self.subject}_{self.session}'
+            final_res_dir = os.path.join(self.output_dir,
+                                         '-'.join(["pymialsrtk", __version__]),
+                                         self.subject,
+                                         self.session)
+        # Get the HTML report template
+        path = pkg_resources.resource_filename(
+            'pymialsrtk',
+            "data/report/templates/template.html"
+        )
+        jinja_template_dir = os.path.dirname(path)
+
+        file_loader = FileSystemLoader(jinja_template_dir)
+        env = Environment(loader=file_loader)
+
+        template = env.get_template('template.html')
+
+        # Load main data derivatives necessary for the report
+        sr_nii_image = os.path.join(
+            final_res_dir, 'anat',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.nii.gz'
+        )
+        img = nib.load(sr_nii_image)
+        sx, sy, sz = img.header.get_zooms()
+
+        sr_json_metadata = os.path.join(
+            final_res_dir, 'anat',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.json'
+        )
+        with open(sr_json_metadata) as f:
+            sr_json_metadata = json.load(f)
+
+        workflow_image = os.path.join(
+            '..', 'figures',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-processing_graph.png'
+        )
+
+        sr_png_image = os.path.join(
+            '..', 'figures',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_T2w.png'
+        )
+
+        motion_report_image = os.path.join(
+            '..', 'figures',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-motion_stats.png'
+        )
+
+        log_file = os.path.join(
+            '..', 'logs',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_log.txt'
+        )
+
+        if os.path.exists(os.path.join(
+            final_res_dir, 'logs',
+            f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-profiling_log.html'
+        )):
+            resources_log_file = os.path.join(
+                '..', 'logs',
+                f'{sub_ses}_rec-SR_id-{self.sr_id}_desc-profiling_log.html'
+            )
+        else:
+            resources_log_file = None
+
+        # Create the text for {{subject}} and {{session}} fields in template
+        report_subject_text = f'{self.subject.split("-")[-1]}'
+        if self.session is not None:
+            report_session_text = f'{self.session.split("-")[-1]}'
+        else:
+            report_session_text = None
+
+        # Generate the report
+        report_html_content = template.render(
+            subject=report_subject_text,
+            session=report_session_text,
+            processing_datetime=self.run_start_time,
+            run_time=self.run_elapsed_time,
+            log=log_file,
+            resources_stats=resources_log_file,
+            sr_id=self.sr_id,
+            stacks=self.m_stacks,
+            svr="on" if not self.m_skip_svr else "off",
+            nlm_denoising="on" if not self.m_skip_nlm_denoising else "off",
+            stacks_ordering="on" if not self.m_skip_stacks_ordering else "off",
+            do_refine_hr_mask="on" if self.m_do_refine_hr_mask else "off",
+            use_auto_masks="on" if self.m_masks_derivatives_dir is None else "off",
+            custom_masks_dir=self.m_masks_derivatives_dir if self.m_masks_derivatives_dir is not None else None,
+            sr_resolution=f"{sx} x {sy} x {sz} mm<sup>3</sup>",
+            sr_json_metadata=sr_json_metadata,
+            workflow_graph=workflow_image,
+            sr_png_image=sr_png_image,
+            motion_report_image=motion_report_image,
+            version=__version__,
+            os=f'{platform.system()} {platform.release()}',
+            python=f'{sys.version}',
+            openmp_threads=self.openmp_number_of_cores,
+            nipype_threads=self.nipype_number_of_cores,
+            jinja_version=__jinja2_version__
+        )
+        # Create the report directory if it does not exist
+        report_dir = os.path.join(final_res_dir, 'report')
+        os.makedirs(report_dir, exist_ok=True)
+
+        # Save the HTML report file
+        out_report_filename = os.path.join(report_dir, f'{sub_ses}.html')
+        print(f'\t* Save HTML report as {out_report_filename}...')
+        with open(out_report_filename, "w+") as file:
+            file.write(report_html_content)
diff --git a/pymialsrtk/utils/__init__.py b/pymialsrtk/utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pymialsrtk/utils/monitoring.py b/pymialsrtk/utils/monitoring.py
new file mode 100644
index 000000000..662e5a810
--- /dev/null
+++ b/pymialsrtk/utils/monitoring.py
@@ -0,0 +1,461 @@
+# Copyright © 2016-2021 Medical Image Analysis Laboratory, University Hospital Center and University of Lausanne (UNIL-CHUV), Switzerland
+#
+#  This software is distributed under the open-source license Modified BSD.
+
+"""PyMIALSRTK utils functions for resource monitoring originated from Nipype.
+
+See https://nipype.readthedocs.io/en/latest/api/generated/nipype.utils.profiler.html for Nipype's documentation.
+See https://nipype.readthedocs.io/en/latest/api/generated/nipype.utils.draw_gantt_chart.html
+"""
+# Import packages
+import datetime
+import simplejson as json
+
+from nipype.utils.draw_gantt_chart import calculate_resource_timeseries,\
+    create_event_dict, draw_nodes, draw_lines
+
+# Pandas
+try:
+    import pandas as pd
+except ImportError:
+    print(
+        "Pandas not found; in order for full functionality of this module "
+        "install the pandas package"
+    )
+    pass
+
+
+# Log node stats function
+def log_nodes_cb(node, status):
+    """Function to record node run statistics to a log file as json
+    dictionaries (source: https://github.com/nipy/nipype/blob/10388b0bb5d341628b68258f655b9f86fb2cf5a6/nipype/utils/profiler.py#L111-L153)
+
+    Modified to ignore mapnodes.
+
+    Parameters
+    ----------
+    node : nipype.pipeline.engine.Node
+        the node being logged
+
+    status : string
+        acceptable values are 'start', 'end'; otherwise it is
+        considered and error
+
+    Returns
+    -------
+    None
+        this function does not return any values, it logs the node
+        status info to the callback logger
+
+    """
+    if status != "end":
+        return
+
+    # Ignore MapNodes by checking for lists, since
+    # the subnodes already called
+    # source: https://github.com/nipy/nipype/issues/2571#issuecomment-396209516
+    if isinstance(node.result.runtime, list):
+        return
+
+    # Import packages
+    import logging
+    import json
+
+    status_dict = {
+        "name": node.name,
+        "id": node._id,
+        "start": getattr(node.result.runtime, "startTime"),
+        "finish": getattr(node.result.runtime, "endTime"),
+        "duration": getattr(node.result.runtime, "duration"),
+        "runtime_threads": getattr(node.result.runtime, "cpu_percent", "N/A"),
+        "runtime_memory_gb": getattr(node.result.runtime, "mem_peak_gb", "N/A"),
+        "estimated_memory_gb": node.mem_gb,
+        "num_threads": node.n_procs,
+    }
+
+    if status_dict["start"] is None or status_dict["finish"] is None:
+        status_dict["error"] = True
+
+    # Dump string to log
+    logging.getLogger("callback").debug(json.dumps(status_dict))
+
+
+def log_to_dict(logfile):
+    """
+    Function to extract log node dictionaries into a list of python
+    dictionaries and return the list as well as the final node
+
+    Parameters
+    ----------
+    logfile : string
+        path to the json-formatted log file generated from a nipype
+        workflow execution
+
+    Returns
+    -------
+    nodes_list : list
+        a list of python dictionaries containing the runtime info
+        for each nipype node
+
+    """
+    # Init variables
+    with open(logfile, "r") as content:
+        # read file separating each line
+        lines = content.readlines()
+
+    nodes_list = []
+    for l in lines:
+        node = json.loads(l)
+        node["start"] = datetime.datetime.strptime(node["start"],
+                                                   '%Y-%m-%dT%H:%M:%S.%f')
+        node["finish"] = datetime.datetime.strptime(node["finish"],
+                                                    '%Y-%m-%dT%H:%M:%S.%f')
+        nodes_list.append(node)
+
+    # Return list of nodes
+    return nodes_list
+
+
+def draw_resource_bar(
+    start_time,
+    finish_time,
+    time_series,
+    space_between_minutes,
+    minute_scale,
+    color,
+    left,
+    resource,
+    max_bar_width
+):
+    """Draw the resource bar in the resource monitoring gantt chart.
+
+    Parameters
+    ----------
+    start_time : datatime.DateTime
+        node start datetime
+
+    finish_time : datatime.DateTime
+        Node finish datetime
+
+    time_series : pandas.Series
+        Node timeseries
+
+    space_between_minutes : int
+        Space between minutes
+
+    minute_scale : int
+        Scaling of space between minutes
+
+    color : string
+        Color hexadecimal code
+
+    left : int
+        Number of pixels to place to bar to the right of
+        the left edge
+
+    resource : 'Memory' or 'Threads'
+        Type of resource
+
+    max_bar_width : int
+        Maximal bar width in pixels
+
+    Returns
+    -------
+    result : string
+        Bar-formatted html string
+
+    """
+    # Memory header
+    result = "<p class='time' style='top:198px;left:%dpx;'>%s</p>" % (left, resource)
+    # Image scaling factors
+    scale = space_between_minutes / minute_scale
+    space_between_minutes = space_between_minutes / scale
+
+    # Maximal value in the time series
+    max_ts = time_series.max()
+
+    # Iterate through time series
+    ts_items = time_series.items()
+    ts_len = len(time_series)
+    for idx, (ts_start, amount) in enumerate(ts_items):
+        if idx < ts_len - 1:
+            ts_end = time_series.index[idx + 1]
+        else:
+            ts_end = finish_time
+        # Calculate offset from start at top
+        offset = (
+            (ts_start - start_time).total_seconds() / 60.0
+        ) * scale * space_between_minutes + 250
+        # Scale duration
+        duration_mins = (ts_end - ts_start).total_seconds() / 60.0
+        height = duration_mins * scale * space_between_minutes
+        if height < 5:
+            height = 5
+        height -= 2
+
+        # Bar width is proportional to resource amount
+        width = amount * (max_bar_width / max_ts)
+
+        if resource.lower() == "memory":
+            label = "%.3f GB" % amount
+        else:
+            label = "%d threads" % amount
+
+        # Setup dictionary for bar html string insertion
+        bar_dict = {
+            "color": color,
+            "height": height,
+            "width": width,
+            "offset": offset,
+            "left": left,
+            "label": label,
+            "duration": duration_mins,
+            "start": ts_start.strftime("%Y-%m-%d %H:%M:%S"),
+            "finish": ts_end.strftime("%Y-%m-%d %H:%M:%S"),
+        }
+
+        bar_html = (
+            "<div class='bar' style='background-color:%(color)s;"
+            "height:%(height).3fpx;width:%(width).3fpx;"
+            "left:%(left)dpx; top:%(offset).3fpx;'"
+            "title='%(label)s\nduration:%(duration).3f minutes\n"
+            "start:%(start)s\nend:%(finish)s'></div>"
+        )
+        # Add another bar to html line
+        result += bar_html % bar_dict
+
+    # Return bar-formatted html string
+    return result
+
+
+def generate_gantt_chart(
+    logfile,
+    cores,
+    minute_scale=10,
+    space_between_minutes=50,
+    colors=["#7070FF", "#4E4EB2", "#2D2D66", "#9B9BFF"],
+    pipeline_name='Undefined',
+):
+    """
+    Generates a gantt chart in html showing the workflow execution based on a callback log file.
+    This script was intended to be used with the MultiprocPlugin.
+    The following code shows how to set up the workflow in order to generate the log file:
+
+    Parameters
+    ----------
+    logfile : string
+        filepath to the callback log file to plot the gantt chart of
+
+    cores : integer
+        the number of cores given to the workflow via the 'n_procs'
+        plugin arg
+
+    minute_scale : integer (optional); default=10
+        the scale, in minutes, at which to plot line markers for the
+        gantt chart; for example, minute_scale=10 means there are lines
+        drawn at every 10 minute interval from start to finish
+
+    space_between_minutes : integer (optional); default=50
+        scale factor in pixel spacing between minute line markers
+
+    colors : list (optional)
+        a list of colors to choose from when coloring the nodes in the
+        gantt chart
+
+    pipeline_name : string
+        The name of the pipeline to be displayed in the output HTML page
+        (Default: `'Undefined'`)
+
+    Returns
+    -------
+    None
+        the function does not return any value but writes out an html
+        file in the same directory as the callback log path passed in
+
+    Usage
+    -----
+    # import logging
+    # import logging.handlers
+    # from nipype.utils.profiler import log_nodes_cb
+    # log_filename = 'callback.log'
+    # logger = logging.getLogger('callback')
+    # logger.setLevel(logging.DEBUG)
+    # handler = logging.FileHandler(log_filename)
+    # logger.addHandler(handler)
+    # #create workflow
+    # workflow = ...
+    # workflow.run(plugin='MultiProc',
+    #     plugin_args={'n_procs':8, 'memory':12, 'status_callback': log_nodes_cb})
+    # generate_gantt_chart('callback.log', 8)
+
+    """
+    # add the html header
+    html_string = """<!DOCTYPE html>
+    <head>
+        <style>
+            #content{
+                width:99%;
+                height:100%;
+                position:absolute;
+            }
+            .node{
+                background-color:#7070FF;
+                border-radius: 5px;
+                position:absolute;
+                width:20px;
+                white-space:pre-wrap;
+            }
+            .line{
+                position: absolute;
+                color: #C2C2C2;
+                opacity: 0.5;
+                margin: 0px;
+            }
+            .time{
+                position: absolute;
+                font-size: 16px;
+                color: #666666;
+                margin: 0px;
+            }
+            .bar{
+                position: absolute;
+                height: 1px;
+                opacity: 0.7;
+            }
+            .dot{
+                position: absolute;
+                width: 1px;
+                height: 1px;
+                background-color: red;
+            }
+            .label {
+                width:20px;
+                height:20px;
+                opacity: 0.7;
+                display: inline-block;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="content">
+            <div style="display:inline-block;">
+    """
+
+    close_header = """
+    </div>
+    <div style="display:inline-block;margin-left:60px;vertical-align: top;">
+        <p><span><div class="label" style="background-color:#90BBD7;"></div> Estimated Resource</span></p>
+        <p><span><div class="label" style="background-color:#03969D;"></div> Actual Resource</span></p>
+        <p><span><div class="label" style="background-color:#f00;"></div> Failed Node</span></p>
+    </div>
+    """
+
+    # Read in json-log to get list of node dicts
+    nodes_list = log_to_dict(logfile)
+
+    # Create the header of the report with useful information
+    start_node = nodes_list[0]
+    last_node = nodes_list[-1]
+
+    print(f'last_node["finish"]: {last_node["finish"]}')
+    print(f'start_node["start"] :{start_node["start"]}')
+
+    duration = (last_node["finish"] - start_node["start"]).total_seconds()
+
+    # Get events based dictionary of node run stats
+    events = create_event_dict(start_node["start"], nodes_list)
+
+    # Summary strings of workflow at top
+    html_string += "<p>Pipeline Folder: " + pipeline_name + "</p>"
+    html_string += (
+        "<p>Start: " + start_node["start"].strftime("%Y-%m-%d %H:%M:%S") + "</p>"
+    )
+    html_string += (
+        "<p>Finish: " + last_node["finish"].strftime("%Y-%m-%d %H:%M:%S") + "</p>"
+    )
+    html_string += "<p>Duration: " + "{0:.2f}".format(duration / 60) + " minutes</p>"
+    html_string += "<p>Nodes: " + str(len(nodes_list)) + "</p>"
+    html_string += "<p>Cores: " + str(cores) + "</p>"
+    html_string += close_header
+    # Draw nipype nodes Gantt chart and runtimes
+    html_string += draw_lines(
+        start_node["start"], duration, minute_scale, space_between_minutes
+    )
+    html_string += draw_nodes(
+        start_node["start"],
+        nodes_list,
+        cores,
+        minute_scale,
+        space_between_minutes,
+        colors,
+    )
+
+    # Get memory timeseries
+    estimated_mem_ts = calculate_resource_timeseries(events, "estimated_memory_gb")
+    runtime_mem_ts = calculate_resource_timeseries(events, "runtime_memory_gb")
+    # Plot gantt chart
+
+    # Determine offset for 200px width
+    max_bar_width = 200
+    resource_offset = 120 + 30 * cores
+    html_string += draw_resource_bar(
+            start_node["start"],
+            last_node["finish"],
+            estimated_mem_ts,
+            space_between_minutes,
+            minute_scale,
+            "#90BBD7",
+            resource_offset + max_bar_width + 120,
+            "Memory",
+            max_bar_width
+    )
+    html_string += draw_resource_bar(
+            start_node["start"],
+            last_node["finish"],
+            runtime_mem_ts,
+            space_between_minutes,
+            minute_scale,
+            "#03969D",
+            resource_offset + max_bar_width + 120,
+            "Memory",
+            max_bar_width
+    )
+
+    # Get threads timeseries
+    estimated_threads_ts = calculate_resource_timeseries(events, "estimated_threads")
+    runtime_threads_ts = calculate_resource_timeseries(events, "runtime_threads")
+
+    print(f'estimated threads: {estimated_threads_ts.max()}')
+    print(f'runtime threads: {runtime_threads_ts.max()}')
+
+    html_string += draw_resource_bar(
+            start_node["start"],
+            last_node["finish"],
+            estimated_threads_ts,
+            space_between_minutes,
+            minute_scale,
+            "#90BBD7",
+            resource_offset,
+            "Threads",
+            max_bar_width
+    )
+    html_string += draw_resource_bar(
+            start_node["start"],
+            last_node["finish"],
+            runtime_threads_ts,
+            space_between_minutes,
+            minute_scale,
+            "#03969D",
+            resource_offset,
+            "Threads",
+            max_bar_width
+    )
+
+    # finish html
+    html_string += """
+        </div>
+    </body>"""
+
+    # save file
+    with open(logfile + ".html", "w") as html_file:
+        html_file.write(html_string)
\ No newline at end of file
diff --git a/setup.py b/setup.py
index ca7e97dc8..944ec5bd0 100644
--- a/setup.py
+++ b/setup.py
@@ -15,16 +15,23 @@
 if os.path.exists('MANIFEST'):
     os.remove('MANIFEST')
 
-packages = ["pymialsrtk",
-            "pymialsrtk.cli",
-            "pymialsrtk.interfaces",
-            "pymialsrtk.pipelines",
-            "pymialsrtk.pipelines.anatomical"]
-
-package_data = {"pymialsrtk":
-                ['data/Network_checkpoints/Network_checkpoints_localization/*',
-                 'data/Network_checkpoints/Network_checkpoints_segmentation/*'],
-                }
+packages = [
+    "pymialsrtk",
+    "pymialsrtk.bids",
+    "pymialsrtk.cli",
+    "pymialsrtk.interfaces",
+    "pymialsrtk.pipelines",
+    "pymialsrtk.pipelines.anatomical",
+    "pymialsrtk.utils"
+]
+
+package_data = {
+    "pymialsrtk": [
+        'data/Network_checkpoints/Network_checkpoints_localization/*',
+        'data/Network_checkpoints/Network_checkpoints_segmentation/*',
+        'data/report/templates/*'
+    ],
+}
 
 # Extract package requirements from Conda environment.yml
 include_conda_pip_dependencies = False
diff --git a/setup_pypi.py b/setup_pypi.py
index b6be3d8fb..4c0cbcce1 100644
--- a/setup_pypi.py
+++ b/setup_pypi.py
@@ -15,12 +15,21 @@
 if os.path.exists('MANIFEST'):
     os.remove('MANIFEST')
 
-packages = ["pymialsrtk",
-            "pymialsrtk.cli",
-            "pymialsrtk.interfaces",
-            "pymialsrtk.pipelines",
-            "pymialsrtk.pipelines.anatomical"]
-
+packages = [
+    "pymialsrtk",
+    "pymialsrtk.bids",
+    "pymialsrtk.cli",
+    "pymialsrtk.interfaces",
+    "pymialsrtk.pipelines",
+    "pymialsrtk.pipelines.anatomical",
+    "pymialsrtk.utils"
+]
+
+package_data = {
+    "pymialsrtk": [
+        'data/report/templates/*'
+    ],
+}
 # Extract package requirements from Conda environment.yml
 include_conda_pip_dependencies = False
 install_requires = []
diff --git a/src/IntensityStandardization/mialsrtkHistogramNormalization.py b/src/IntensityStandardization/mialsrtkHistogramNormalization.py
index f11443c1d..0d851edb9 100644
--- a/src/IntensityStandardization/mialsrtkHistogramNormalization.py
+++ b/src/IntensityStandardization/mialsrtkHistogramNormalization.py
@@ -183,12 +183,28 @@ def computeMeanMapImageLandmarks(list_landmarks):
     print(mean_landmarks)
     return mean_landmarks
 
+def sort_ascending(p_files):
+    """Sort files by file basename instead of file path.
+    Added March 2021 by P. de Dumast
+    """
+    from operator import itemgetter
+    import os
+    path_basename = []
+    for f in p_files:
+        path_basename.append((os.path.basename(f), f))
+
+    path_basename = sorted(path_basename, key=itemgetter(0))
+    return [f[1] for f in path_basename]
+
 
 def main(images,masks,outputs):
     
-    image_paths= sorted(images)
-    mask_paths=sorted(masks)
-    output_paths = sorted(outputs)
+    # image_paths= sorted(images)
+    # mask_paths=sorted(masks)
+    # output_paths = sorted(outputs)
+    image_paths= sort_ascending(images)
+    mask_paths=sort_ascending(masks)
+    output_paths = sort_ascending(outputs)
 
     if(len(image_paths)!=len(mask_paths)):
         print('Loading failed: Number of images and masks are different (# images = ', str(len(image_paths)), '\\ # masks =', str(len(mask_paths)), ')')


Test patch:
diff --git a/.circleci/test-01_outputs.txt b/.circleci/test-01_outputs.txt
index 9b5cc500b..293ffe8c8 100644
--- a/.circleci/test-01_outputs.txt
+++ b/.circleci/test-01_outputs.txt
@@ -11,432 +11,443 @@ manual_masks/sub-01/anat/sub-01_run-5_T2w_desc-brain_mask.json
 manual_masks/sub-01/anat/sub-01_run-5_T2w_desc-brain_mask.nii.gz
 manual_masks/sub-01/anat/sub-01_run-6_T2w_desc-brain_mask.json
 manual_masks/sub-01/anat/sub-01_run-6_T2w_desc-brain_mask.nii.gz
-nipype/sub-01/rec-1/pypeline.log
-nipype/sub-01/rec-1/srr_pipeline/d3.js
-nipype/sub-01/rec-1/srr_pipeline/data_grabber/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/data_grabber/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/data_grabber/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/data_grabber/result_data_grabber.pklz
-nipype/sub-01/rec-1/srr_pipeline/data_sinker/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/data_sinker/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/data_sinker/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/data_sinker/result_data_sinker.pklz
-nipype/sub-01/rec-1/srr_pipeline/filenames_gen/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/filenames_gen/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/filenames_gen/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/filenames_gen/result_filenames_gen.pklz
-nipype/sub-01/rec-1/srr_pipeline/graph1.json
-nipype/sub-01/rec-1/srr_pipeline/graph.dot
-nipype/sub-01/rec-1/srr_pipeline/graph.json
-nipype/sub-01/rec-1/srr_pipeline/graph.png
-nipype/sub-01/rec-1/srr_pipeline/index.html
-nipype/sub-01/rec-1/srr_pipeline/masks_filtered/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/masks_filtered/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/masks_filtered/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/masks_filtered/result_masks_filtered.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise0/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise0/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise0/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise0/result__nlmDenoise0.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise0/sub-01_run-1_T2w_nlm.nii.gz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise1/_inputs.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise1/_node.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise1/_report/report.rst
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise1/result__nlmDenoise1.pklz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise1/sub-01_run-2_T2w_nlm.nii.gz
-nipype/sub-01/rec-1/srr_pipeline/nlmDenoise/mapflow/_nlmDenoise2/_inputs.pklz
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diff --git a/.circleci/test-03_outputs.txt b/.circleci/test-03_outputs.txt
index 9b5cc500b..293ffe8c8 100644
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