omegatomo 2.1.0.0
Open-source multi-dimensional tomographic reconstruction software for Python.
Requires Python: >=3.6
Keywords: tomography, tomographic, PET, CT, SPECT, image reconstruction

# OMEGA
Open-source multi-dimensional tomographic reconstruction software for MATLAB, GNU Octave and Python.

## Table of contents
- [Introduction](#introduction)
    - [Projector models](#projector-models)
    - [Reconstruction algorithms](#reconstruction-algorithms)
    - [Prior models](#prior-models)
- [Getting started](#getting-started)
- [System requirements](#system-requirements)
- [Installation](#installation)
    - [Windows / Linux](#windows--linux)
    - [MacOS](#MacOS)
- [Additional and upcoming features](#additional-and-upcoming-features)
    - [Independent functions](#independent-functions)
    - [Upcoming features](#upcoming-features)
- [Known issues and limitations](#known-issues-and-limitations)
    - [Python & MATLAB & Octave](#python--matlab--octave)
    - [MATLAB & Octave](#matlab--octave)
    - [MATLAB](#matlab)
    - [Python](#python)
    - [Intel](#intel)
    - [Apple / Metal / MacOS](#apple--metal--macos)
- [Reporting bugs and feature requests](#reporting-bugs-and-feature-requests)
- [How to cite OMEGA](#citations)
- [Acknowledgments](#acknowledgments)
- [References](#references)

## Introduction

OMEGA is a piece of software for [MATLAB](https://www.mathworks.com/), [GNU Octave](https://www.gnu.org/software/octave/) and [Python](https://www.python.org/) to reconstruct tomographic data. The purpose of OMEGA is twofold. First, it is designed to allow easy, fast, and efficient reconstruction for any ray-tracing based tomographic imaging modality. Second, it is intended for easy algorithmic development by allowing easy matrix-free implementation of the forward (`A * x`) and backward (`A^T * y`) projection operations. While OMEGA allows the use of any ray-tracing based tomographic data, it is optimized for positron emission tomography (PET), computed tomography (CT) and single photon emission computed tomography (SPECT).

See [Features](https://omega-doc.readthedocs.io/en/latest/features.html) for more information on available features. See Known Issues and Limitations below for software limitations. If you wish to add your own code (e.g. reconstruction algorithm) see [Contributing code to OMEGA](https://github.com/villekf/OMEGA/wiki/Contributing-code-to-OMEGA).

Documentation for the current version is available at https://omega-doc.readthedocs.io/en/latest/index.html

The algorithms implemented so far include:
### Projector models
- Improved Siddon's ray tracer algorithm for the system matrix creation (code for regular Siddon available, but not used) [1,2]
- Orthogonal distance-based ray tracer [3]
- Volume of intersection ray tracer (THOR) [28].
- Interpolation-based ray tracer [31]
- Branchless distance-driven ray tracer [32,33]
- Rotation-based projector [34]
### Reconstruction algorithms
- Maximum Likelihood Expectation Maximization (MLEM) [4,5]
- Ordered Subsets Expectation Maximization (OSEM) [6]
- Complete-data Ordered Subsets Expectation Maximization (COSEM) [7]
- Enhanced COSEM (ECOSEM) [8]
- Accelerated COSEM (ACOSEM) [9]
- Row-Action Maximum Likelihood Algorithm (RAMLA) [10]
- Relaxed OSEM (ROSEM)
- Rescaled Block-Iterative EM (RBI) [11]
- Dynamic RAMLA (DRAMA) [12]
- Modified RAMLA (MRAMLA), aka modified BSREM-2 [13] 
- Block Sequential Regularized Expectation Maximization (BSREM) [14]
- One-step-late algorithm (OSL) [15]
- Preconditioned Krasnoselskii-Mann algorithm (PKMA) [29]
- Primal-dual hybrid gradient (PDHG, aka Chambolle-Pock algorithm) [35]
- Condat-Vu (CV) [36,37]
- Primal-dual Davis-Yin (PDDY) [38]
- Fast iterative shrinkage thresholding algorithm (FISTA) [39]
- Feldkamp-Davis-Kress (FDK) [40]
- LSQR [46]
- Conjugate Gradient Least-squares (CGLS) [47]
- (Ordered subsets-)Simultaneous Algebraic Reconstruction Technique ((OS-)SART) [48,49]
- ASD-POCS [50]
- Barzilai-Borwein (BB) method [51]
### Prior models
- Quadratic prior (Gibbs prior with quadratic potential function)
- Huber prior [45]
- Median Root Prior (MRP) [16]
- L-filter (MRP-L) prior [17]
- Finite Impulse Response Median Hybrid (MRP-FMH) prior [17,18]
- Weighted mean prior [19,20]
- Total variation (TV) [21, 22, 23]
- Weighted TV [42]
- Total generalized variation (TGV) [24]
- Anisotropic diffusion (AD) Median Root Prior
- Asymmetric parallel levels sets prior (APLS) [22]
- Non-local means prior (NLM), including non-local TV [25,26,27]
- Relative difference prior (RDP) [30]
- Generalized Gaussian Markov random field prior (GGMRF) [41]
- Modified hyperbolic prior [23,43]
- Modified Lange prior [29,44]

## Getting Started

For detailed installation instructions, see https://omega-doc.readthedocs.io/en/latest/installation.html. For basic usage, see https://omega-doc.readthedocs.io/en/latest/usage.html

Precompiled libraries are included in [releases](https://github.com/villekf/OMEGA/releases) and in the pip version in Python and in the MATLAB toolbox for MATLAB. For Python, the pip version is recommended (`pip install omegatomo`) and for MATLAB the toolbox file located in [releases](https://github.com/villekf/OMEGA/releases).

However, in case those do not work you can also manually compile all the necessary files. When using MATLAB or GNU Octave, run `install_mex` to compile the necessary files. For Python, you need run `compile.py` located in `/path/to/OMEGA/source/Python`.

Examples for MATLAB/GNU Octave are located in `/path/to/OMEGA/main-files` and for Python in `/path/to/OMEGA/source/Python`. If you use the pip version, you'll need to manually obtain the example files.

## System Requirements
OMEGA can be run from either MATLAB, GNU Octave or Python.

The software has been tested with MATLAB versions 2022a, 2023b, 2024a, 2024b and 2025a. For Octave, any version above 5.0 should be fine though io, statistics and image packages are required for some features. For Python 3.8 and above should work, though most likely earlier versions will work too. NumPy version 1.20 is required for the Python version, but newer versions of NumPy work fine too.

A C++11 compiler is required when manually compiling. On Windows, [Visual Studio](https://visualstudio.microsoft.com/) 2019 or 2022 is recommended with "Desktop development with C++", no other options are required. On Linux, use the g++ compiler that comes with your distribution whenever possible. Note that MATLAB only supports certain compiler versions, so please check the [supported compilers table](https://www.mathworks.com/support/requirements/supported-compilers-linux.html). On MacOS, [Xcode](https://apps.apple.com/us/app/xcode/id497799835?mt=12) should be used.

OpenCL functionality requires OpenCL libraries and an OpenCL 1.2 (or higher) compatible device. CUDA functionality requires CUDA libraries and a device with compute capability of 2.0 (or higher). [ArrayFire](https://arrayfire.com/binaries/) libraries are recommended for MATLAB, and required for Python.

The following third-party MATLAB codes are NOT required, but can be useful in certain specialized cases as they can be optionally used:
- [Shuffle](https://www.mathworks.com/matlabcentral/fileexchange/27076-shuffle), used for random subset sampling
- [vol3d v2](https://www.mathworks.com/matlabcentral/fileexchange/22940-vol3d-v2), used for 3D visualization
- [Tools for NIfTI and ANALYZE image](https://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image) to load/save Analyze files and also NIfTI files in absence of image processing toolbox.

## Installation

### Windows / Linux

For additional install help, see [installation help](https://omega-doc.readthedocs.io/en/latest/installation.html).

As a first step you should install either OpenCL (install either https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases/tag/1.0 for Windows or in Linux use `sudo apt install ocl-icd-opencl-dev`) or [CUDA](https://developer.nvidia.com/cuda-downloads), and then [ArrayFire](https://arrayfire.com/binaries/). See more details in the above link or below.

Pre-built libraries are supplied in the [releases](https://github.com/villekf/OMEGA/releases), however, you can also manually compile everything. Python pip version and MATLAB toolbox contain prebuilt libraries, which should work in most cases.

**All library paths needs to be on system path when running the mex-files or Python scripts or otherwise the required libraries will not be found. This means ArrayFire and OpenCL and/or CUDA.**

Read the below part only if you encounter issues or are using Octave:

For manual compilation you're going to need a C++ compiler in order to compile the MEX-files/libraries and use this software. Visual Studio and GCC have been tested to work and are recommended depending on your platform (Visual Studio in Windows, GCC in Linux, clang should work in MacOS). Specifically, Visual Studio 2019 and 2022 have been tested to work in Windows 10 and as well as g++ 9.3 and g++ 10.5 on Ubuntu 22.04. MinGW++ also works though it is unable to compile ArrayFire OpenCL reconstructions (implementation 2) in Windows by default. Octave supports only MinGW++ in Windows and as such implementation 2 in Windows is only supported if you manually compile ArrayFire from source with MinGW (for instructions, see [here](https://github.com/villekf/OMEGA/wiki/Building-ArrayFire-with-Mingw-on-Windows)).

MinGW++ for MATLAB can be downloaded from [here](https://se.mathworks.com/matlabcentral/fileexchange/52848-matlab-support-for-mingw-w64-c-c-compiler).

Visual Studio can be downloaded from [here](https://visualstudio.microsoft.com/). For Visual studio you'll only need "Desktop development with C++".

On Ubuntu you can install g++ with `sudo apt install build-essential`.

To install the OMEGA software, either simply extract the release/master package, download the MATLAB toolbox file from [releases](https://github.com/villekf/OMEGA/releases) (`OMEGA.-.Open-source.MATLAB.emission.tomography.software.mltbx`, MATLAB only), install for Python through pip (`pip install omegatomo`), or obtain the source code through git:  
`git clone https://github.com/villekf/OMEGA`
and then add the OMEGA folder and subfolders to MATLAB/Octave path (this is done automatically if you install with the mltbx-file) or /path/to/OMEGA/source/Python to PYTHONPATH (this step is not required when installing through pip). 
Finally, run `install_mex` in the source folder to build the necessary MEX-files or compile.py with Python. ROOT, OpenCL and CUDA support will be installed, if the corresponding files are found. 
Possible compilation errors can be seen with `install_mex(1)`. OpenCL include and library paths, ArrayFire path and ROOT path can also be set manually with 
`install_mex(0, OpenCL_include_path, OpenCL_lib_path, AF_PATH, ROOT_PATH)`. `OpenCL_include_path` should be the folder where `cl.h` is located, `OpenCL_lib_path` the folder where `OpenCL.lib/libOpenCL.so` (Windows/Linux) is located, 
`AF_PATH` the path to ArrayFire installation location and `ROOT_PATH` to ROOT installation location. For Python the paths can be input with "python compile.py -R /path/to/ROOT -A /path/to/arrayfire -O /path/to/OpenCL".

Certain features on Octave (such as normalization calculation) require packages io and statistics. You can install them from the Octave user interface with the following commands (io has to be installed first):

`pkg install -forge io`

`pkg install -forge statistics`

and then you need to load the statistics package:

`pkg load statistics`

Python only requires NumPy (1.20 or newer), though to load mat-files you need `pymatreader` and for multi-resolution reconstruction `scikit-image`.

In order to enable OpenCL support (implementations 2 and 3), you're going to need an OpenCL SDK/library and (for implementation 2) ArrayFire (see below). 
in Linux you can alternatively just install the OpenCL headers and library. Below examples are for Ubuntu, but the packages should exist for other distros as well.

Headers (required only when manually building):
`sudo apt-get install opencl-headers`

and then the library:  
`sudo apt-get install ocl-icd-opencl-dev`

Alternative libraries in case the above one fails:
`sudo apt-get install nvidia-opencl-dev`
or
`sudo apt-get install intel-opencl-icd`

In case the above doesn't work or you use Windows then you need to obtain an OpenCL SDK. The SDK can be any (or all) of the following: CUDA Toolkit, Intel OpenCL SDK, OCL-SDK, AMD APP SDK (see below for links). 
On all cases, the OpenCL library and header files (only when manually building) need to be on your system's PATH. By default, the install_mex-file assumes that you have installed CUDA toolkit (Linux and Windows), 
AMD APP SDK v3.0 (Linux and Windows), OCL-SDK (Windows), AMD GPU Pro drivers (Linux) or Intel SDK (Linux and Windows). If you get an error message like "CL/cl.h: No such file or directory", the headers could not be found. 
You can manually add custom OpenCL paths with `install_mex(0, '/path/to/cl.h', '/path/to/libOpenCL.so')`. On Ubuntu you can use command `find / -iname cl.h 2>/dev/null` to find the required cl.h file and 
`find / -iname libOpenCL.so 2>/dev/null` to find the required library file. See `install_mex.m` for further details. `compile.py` functions similarly.

CUDA functionality requires CUDA toolkit.

Links:  
https://developer.nvidia.com/cuda-toolkit  
https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases  
https://github.com/KhronosGroup/OpenCL-SDK (for this, you'll need to extract the contents and make sure they can be found by either MATLAB/Octave or Python)

Once you have the header and library files, you need drivers/OpenCL runtimes for your device(s). If you have GPUs/APUs then simply having the vendor drivers should be enough. 
For Intel CPUs without an integrated GPU you need CPU runtimes (see the link below). 

For AMD CPUs you need to use PoCL http://portablecl.org/ or try the Intel runtimes (link below). Note that PoCL can be used for Intel CPUs as well.

Intel runtimes can be found here:
https://software.intel.com/en-us/articles/opencl-drivers


This software also uses ArrayFire library for the GPU/OpenCL implementation. You can find AF binaries from here:  
https://arrayfire.com/binaries
and the source code from here:  
https://github.com/arrayfire/arrayfire

Installing/building ArrayFire to the default location (`C:\Program Files\ArrayFire` in Windows, `/opt/arrayfire/` in Linux/MacOS) should cause `install_mex` and `compile.py` to automatically locate everything. 
However, in both cases you need to add the library paths to the system PATH. In Windows you will be prompted for this during the installation, for Linux you need to add `/opt/arrayfire/lib` or 
`/opt/arrayfire/lib64` (depending which exists) to the library path (e.g. `sudo ldconfig /opt/arrayfire/lib64/` or `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/arrayfire/lib64` if you don't have sudo permissions). 
Alternatively, in Linux, you can also build/install ArrayFire directly into the `/usr/local/` folder.

Using CUDA code instead of OpenCL requires the CUDA toolkit. On both cases the CUDA folder should be on the system path. `install_mex` and `compile.py` always attempts to build the CUDA code as well so no 
additional input is required from the user if all the header and library data is found. By default `install_mex` and `compile.py` looks for CUDA in `/usr/local/cuda/` in Linux. 
In Windows, CUDA location is determined from the environmental variables (PATH).

For additional install help, see [installation help](https://omega-doc.readthedocs.io/en/latest/installation.html).

Portions of version 2 were tested with the following GPUs: Nvidia Tesla A100, AMD Instinct MI100, Nvidia Tesla P100, Nvidia Tesla M40, Nvidia GeForce RTX 4060, Nvidia GeForce RTX 4090, AMD Radeon 7900 XT, Nvidia Titan RTX, Nvidia Quadro A6000 Ada, and Intel Arc A380.
All the GPUs were tested on Linux except AMD Radeon 7900 XT which was tested on Windows.

### MacOS

For installation on MacOS you need Xcode. You also need the single-header version of metal-cpp. The `Metal.hpp` header file should be placed in the folder `source/cpp`. ArrayFire-based reconstructions are not supported due to no available ArrayFire binaries with Metal backend.

## Additional and upcoming features

See [Features](https://omega-doc.readthedocs.io/en/latest/features.html) for more information on available features.

### Independent functions
The following features can be used as independent functions without any input needed from any other OMEGA files

- Save images (matrices) in MATLAB/Octave in NIfTI, MetaImage, Interfile, Analyze 7.5, DICOM and raw binary formats ([saveImage.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/saveImage.m))
- Import NIfTI, MetaImage, Interfile, Analyze 7.5, DICOM and raw binary formats into MATLAB/Octave ([importData.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/importData.m))
- Save images (matrices) in MATLAB/Octave in Interfile ([saveInterfile.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/saveInterfile.m)) or MetaImage formats ([saveMetaimage.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/saveMetaimage.m))
- Convert CT-attenuation coefficients into 511 keV attenuation coefficients ([attenuationCT_to_511.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/attenuationCT_to_511.m))
- (Experimental) Convert CT-attenuation coefficients directly from CT DICOM images into 511 keV attenuation coefficients ([create_atten_matrix_CT.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/m-files/create_atten_matrix_CT.m))
- Convert COO (Coordinate list) sparse matrix row indices into CSR (Compressed sparse row) indices ([coo_to_csr.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/coo_to_csr.m))
- Convert voxelized phantoms/sources into GATE compatible files ([Voxelized_phantom_handle.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/Voxelized_phantom_handle.m), [Voxelized_source_handle.m](https://github.com/villekf/OMEGA/blob/master/source/m-files/Voxelized_source_handle.m))
- Python allows the use of various regularizers independently of any OMEGA code or files. See the code at: https://github.com/villekf/OMEGA/blob/master/source/Python/omegatomo/util/priors.py

### Upcoming Features

Here is a list of features that should appear in future releases:

- Test suite
- Additional SPECT features
    - Dynamic SPECT reconstruction
    - Built-in support for more collimator geometries
- PET scatter correction based on SSS
- Improved support for curved helical CT
- Better support for analytical methods
- Improved dual-layer PET support
- Potential fp16 support
- Deep learning based features

## Known Issues and Limitations

### Python & MATLAB & Octave

Moving bed is not supported at the moment (needs to be step-and-shoot and the different bed positions need to be handled as separate cases). Though it should be possible to manually achieve a moving bed examination.

Only cylindrical symmetric scanners are supported inherently for PET, for other types of scanners the user has to input the detector coordinates or use [index-based reconstruction](https://omega-doc.readthedocs.io/en/latest/customcoordinates.html#index-based-reconstruction).

For CT, only cone/fan beam flat/curved panel scanners are supported. For curved, only cylindrical scanners are supported, but support for spherical scanners is easy to add if needed. For other types of scanners, the user has to input the detector coordinates or modify the data such that it is approximately flat panel. There is some support for parallel beam cases, but the current implementation is slow.

### MATLAB & Octave

LMF support has been deprecated in version 2.0. LMF output currently has to contain the time stamp (cannot be removed in GATE) and detector indices. The source location needs to be included if it was selected in the main-file, same goes for the scatter data. If you have any other options selected in the LMF output in GATE, then you will not get any sensible detector data. Source locations and/or scatter data can be deselected. LMF data, with different format than in GATE, are not supported. LMF source information is a lot more unreliable than the ASCII or ROOT version. 

ROOT or ASCII data is not yet supported with GATE CT data.

ECAT GATE PET geometry is supported only with ASCII data. ROOT data might also work (untested).

If you are experiencing crashes when using implementation 2, it might be caused by the graphics features of ArrayFire (AF). In this case I recommend renaming/removing the libforge.so files from the ArrayFire library folder (e.g. `/opt/arrayfire/lib64/`). Alternatively you can install the no-gl AF:  
http://arrayfire.s3.amazonaws.com/index.html (3.6.2 is the latest). Finally, you can also simply build AF from source, preferably without building Forge. This seems to apply only to Linux and affects both MATLAB and Octave. Python is unaffected!

Implementation 3 doesn't support TOF data. In general, implementation 3 is not recommended anymore and will probably be deprecated in a future release.

### MATLAB

If you get GLIBCXX_3.4.XX/CXXABI_1.3.XX not found error (or similar with a different version number) or an error about "undefined reference to dlopen/dlclose/dlsomethingelse" when building or running files, this should be fixed with one of the methods presented here:  
https://se.mathworks.com/matlabcentral/answers/329796-issue-with-libstdc-so-6

Or see the solutions in [installation help](https://omega-doc.readthedocs.io/en/latest/installation.html#linux).

If you are using ROOT data with ROOT 6.16.00 or newer you might receive the following (or similar) error message: "undefined symbol: _ZN3tbb10interface78internal20isolate_within_arenaERNS1_13delegate_baseEl". This is caused by the `libtbb.so.2` used by MATLAB (located in `/matlabroot/bin/glnxa64`). Same solutions apply as with the above case (e.g. renaming the file). See [installation help](https://omega-doc.readthedocs.io/en/latest/installation.html#linux) for details.

ROOT data import is unstable in MATLAB R2018b and earlier versions due to a library incompatibility between the Java virtual machine in MATLAB and ROOT. In Linux you will experience MATLAB crashes when importing ROOT data. There is a workaround for this by using MATLAB in the no Java mode (e.g `matlab -nojvm`), though you won't have any GUI or graphic features. MATLAB R2019a and up are unaffected. It is recommended to use `nojvm` for data load only (set `options.only_sinos = true` to load only the data). The new desktop might not have this issue, but this is currently untested. This affects only Linux and only MATLAB.

### Octave

When using Windows, implementation 2 (ArrayFire matrix free OpenCL) can only be enabled by manually building ArrayFire with Mingw. Instructions are provided [here](https://github.com/villekf/OMEGA/wiki/Building-ArrayFire-with-Mingw-on-Windows). Note that CUDA won't work even with manual building.

Implementations 3 and 5 fail to build with Octave 9.2 in Windows. Octave 8.3 should work fine.

Almost all MATLAB-based code runs significantly slower compared to MATLAB (this is due to the slowness of loops in Octave). Reconstructions are unaffected.

MAT-files that are over 2 GB are not supported by Octave and such large data sets cannot be saved in Octave at the moment.

### Python

Only implementation 2 is supported.

Status messages, such as the current iteration number, might be displayed only after the computation is already done.

### Intel

Intel GPUs might not support forward and/or backward projection masks. 

### Apple / Metal / MacOS

- Currently supports only custom projection operations (`A * x`, `A^T * y`) due to no native ArrayFire binaries with Metal backend
- No PET support
- No CT support
- Only SPECT support
- No automatic sensitivity image computation
- No support for projector types 4 and 5
- No Python support
- Normalization correction not tested
- Scatter correction not tested
- Incomplete half-precision (FP16) support


## Reporting Bugs and Feature Requests

For any bug reports I recommend posting an issue on GitHub. For proper analysis I need the main-file that you have used and if you have used GATE data then also the macros. Preferably also all possible .mat files created, especially if the problem occurs in the reconstruction phase.

For feature requests, post an issue on GitHub. I do not guarantee that a specific feature will be added in the future.

## How to cite OMEGA

If you wish to use this software in your work, cite this paper: V-V Wettenhovi et al 2021 Phys. Med. Biol. 66 065010. The peer reviewed (open access) paper on OMEGA can be found from https://doi.org/10.1088/1361-6560/abe65f.

If you use some specific algorithm or prior, please cite one of references here or some other original paper!


## Acknowledgments

Original versions of COSEM, ACOSEM, ECOSEM, RAMLA, MRAMLA, MRP, L-filter, FMH, weighted mean, quadratic prior, sinogram coordinate and sinogram creation MATLAB codes were written by Samuli Summala. Normalization coefficient and variance reduction codes were written by Anssi Manninen. Initial work on TOF was done by Jonna Kangasniemi. Initial work on SPECT was done by Matti Kortelainen and Akuroma George. First version of Volume3Dviewer was done by Nargiza Djurabekova. The ray tracer projectors for SPECT were implemented by [Niilo Saarlemo](https://github.com/saarlemo). Barzilai-Borwein algorithm implemented by [Razieh Azizi](https://github.com/RaziAzizi). All other codes were written by [Ville-Veikko Wettenhovi](https://github.com/villekf). Some pieces of code were copied from various websites (Stack Overflow, MATLAB Answers), the original sources of these codes can be found in the source files.

This work was supported by a grant from [Jane and Aatos Erkko foundation](https://jaes.fi/en/), [Instrumentarium Science Foundation](http://instrufoundation.fi/en.php), 
[Jenny and Antti Wihuri Foundation](https://wihurinrahasto.fi/?lang=en) and [The Finnish Research Impact Foundation](https://www.vaikuttavuussaatio.fi/en/). 
This work has been supported by [University of Eastern Finland](https://www.uef.fi/en) and Academy of Finland. 
This work was supported by the Research Council of Finland ([Flagship of Advanced Mathematics for Sensing, Imaging, and Modelling](https://fameflagship.fi/) grant 358944).


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