pinecone/core-2020-05-10-deduplication huggingface dataset:
A deep convolutional neural network using directional wavelets for
  low-dose X-ray CT reconstruction

Due to the potential risk of inducing cancers, radiation dose of X-ray CT
should be reduced for routine patient scanning. However, in low-dose X-ray CT,
severe artifacts usually occur due to photon starvation, beamhardening, etc,
which decrease the reliability of diagnosis. Thus, high quality reconstruction
from low-dose X-ray CT data has become one of the important research topics in
CT community. Conventional model-based denoising approaches are, however,
computationally very expensive, and image domain denoising approaches hardly
deal with CT specific noise patterns. To address these issues, we propose an
algorithm using a deep convolutional neural network (CNN), which is applied to
wavelet transform coefficients of low-dose CT images. Specifically, by using a
directional wavelet transform for extracting directional component of artifacts
and exploiting the intra- and inter-band correlations, our deep network can
effectively suppress CT specific noises. Moreover, our CNN is designed to have
various types of residual learning architecture for faster network training and
better denoising. Experimental results confirm that the proposed algorithm
effectively removes complex noise patterns of CT images, originated from the
reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient
in removing the noises from low-dose CT compared to an image domain CNN. Our
results were rigorously evaluated by several radiologists and won the second
place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our
knowledge, this work is the first deep learning architecture for low-dose CT
reconstruction that has been rigorously evaluated and proven for its efficacy.Comment: Will appear in Medical Physics (invited paper); 2016 AAPM low-dose CT
  Grand Challenge 2nd Place Awar
Investigating Simulation-Based Metrics for Characterizing Linear
  Iterative Reconstruction in Digital Breast Tomosynthesis

Simulation-based image quality metrics are adapted and investigated for
characterizing the parameter dependences of linear iterative image
reconstruction for DBT. Three metrics based on 2D DBT simulation are
investigated: (1) a root-mean-square-error (RMSE) between the test phantom and
reconstructed image, (2) a gradient RMSE where the comparison is made after
taking a spatial gradient of both image and phantom, and (3) a
region-of-interest (ROI) Hotelling observer (HO) for
signal-known-exactly/background-known-exactly (SKE/BKE) and
signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks.
Two simulation studies are performed using the aforementioned metrics, varying
voxel aspect ratio and regularization strength for two types of Tikhonov
regularized least-squares optimization. The RMSE metrics are applied to a 2D
test phantom and the ROI-HO metric is applied to two tasks relevant to DBT:
large, low contrast lesion detection and small, high contrast
microcalcification detection. The RMSE metric trends are compared with visual
assessment of the reconstructed test phantom. The ROI-HO metric trends are
compared with 3D reconstructed images from ACR phantom data acquired with a
Hologic Selenia Dimensions DBT system. Sensitivity of image RMSE to mean pixel
value is found to limit its applicability to the assessment of DBT image
reconstruction. Image gradient RMSE is insensitive to mean pixel value and
appears to track better with subjective visualization of the reconstructed
bar-pattern phantom. The ROI-HO metric shows an increasing trend with
regularization strength for both forms of Tikhonov-regularized least-squares;
however, this metric saturates at intermediate regularization strength
indicating a point of diminishing returns for signal detection. Visualization
with reconstructed ACR phantom images appears to show a similar dependence with
regularization strength.Comment: The manuscript has been submitted to Medical Physic
Fast dose optimization for rotating shield brachytherapy

Purpose: To provide a fast computational method, based on the proximal graph
solver (POGS) - a convex optimization solver using the alternating direction
method of multipliers (ADMM), for calculating an optimal treatment plan in
rotating shield brachytherapy (RSBT). RSBT treatment planning has more degrees
of freedom than conventional high-dose-rate brachytherapy (HDR-BT) due to the
addition of emission direction, and this necessitates a fast optimization
technique to enable clinical usage. // Methods: The multi-helix RSBT (H-RSBT)
delivery technique was considered with five representative cervical cancer
patients. Treatment plans were generated for all patients using the POGS method
and the previously considered commercial solver IBM CPLEX. The rectum, bladder,
sigmoid, high-risk clinical target volume (HR-CTV), and HR-CTV boundary were
the structures considered in our optimization problem, called the asymmetric
dose-volume optimization with smoothness control. Dose calculation resolution
was 1x1x3 mm^3 for all cases. The H-RSBT applicator has 6 helices, with 33.3 mm
of translation along the applicator per helical rotation and 1.7 mm spacing
between dwell positions, yielding 17.5 degree emission angle spacing per 5 mm
along the applicator.// Results: For each patient, HR-CTV D90, HR-CTV D100,
rectum D2cc, sigmoid D2cc, and bladder D2cc matched within 1% for CPLEX and
POGS. Also, we obtained similar EQD2 figures between CPLEX and POGS. POGS was
around 18 times faster than CPLEX. Over all patients, total optimization times
were 32.1-65.4 seconds for CPLEX and 2.1-3.9 seconds for POGS. // Conclusions:
POGS substantially reduced treatment plan optimization time around 18 times for
RSBT with similar HR-CTV D90, OAR D2cc values, and EQD2 figure relative to
CPLEX, which is significant progress toward clinical translation of RSBT. POGS
is also applicable to conventional HDR-BT.Comment: 9 pages, 3 figure
PEAR: PEriodic And fixed Rank separation for fast fMRI

In functional MRI (fMRI), faster acquisition via undersampling of data can
improve the spatial-temporal resolution trade-off and increase statistical
robustness through increased degrees-of-freedom. High quality reconstruction of
fMRI data from undersampled measurements requires proper modeling of the data.
We present an fMRI reconstruction approach based on modeling the fMRI signal as
a sum of periodic and fixed rank components, for improved reconstruction from
undersampled measurements. We decompose the fMRI signal into a component which
a has fixed rank and a component consisting of a sum of periodic signals which
is sparse in the temporal Fourier domain. Data reconstruction is performed by
solving a constrained problem that enforces a fixed, moderate rank on one of
the components, and a limited number of temporal frequencies on the other. Our
approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI.
  Experimental results include purely synthetic simulation, a simulation with
real timecourses and retrospective undersampling of a real fMRI dataset.
Evaluation was performed both quantitatively and visually versus ground truth,
comparing PEAR to two additional recent methods for fMRI reconstruction from
undersampled measurements. Results demonstrate PEAR's improvement in estimating
the timecourses and activation maps versus the methods compared against at
acceleration ratios of R=8,16 (for simulated data) and R=6.66,10 (for real
data). PEAR results in reconstruction with higher fidelity than when using a
fixed-rank based model or a conventional Low-rank+Sparse algorithm. We have
shown that splitting the functional information between the components leads to
better modeling of fMRI, over state-of-the-art methods
Neural network dose models for knowledge-based planning in pancreatic
  SBRT

Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a
skillful approach to deliver ablative doses to the tumor while limiting dose to
the highly sensitive duodenum, stomach, and small bowel. Here, we develop
knowledge-based artificial neural network dose models (ANN-DMs) to predict dose
distributions that would be approved by experienced physicians. Using dose
distributions calculated by a commercial treatment planning system (TPS),
physician-approved treatment plans were used to train ANN-DMs that could
predict physician-approved dose distributions based on a set of geometric
parameters (vary from voxel to voxel) and plan parameters (constant across all
voxels for a given patient). Differences between TPS and ANN-DM dose
distributions were used to evaluate model performance. ANN-DM design, including
neural network structure and parameter choices, were evaluated to optimize dose
model performance. Mean dose errors were less than 5% at all distances from the
PTV, and mean absolute dose errors were on the order of 5%, but no more than
10%. Dose-volume histogram errors demonstrated good model performance above 25
Gy, but much larger errors were seen at lower doses. ANN-DM dose distributions
showed excellent overall agreement with TPS dose distributions, and accuracy
was substantially improved when each physician's treatment approach was taken
into account by training their own dedicated models. In this manner, one could
feasibly train ANN-DMs that could predict the dose distribution desired by a
given physician for a given treatment site.Comment: 11 pages, 6 figure
Maximization of Regional probabilities using Optimal Surface Graphs: Application to Carotid Artery Segmentation in MRI

__Purpose__ We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images.  \ud
__Methods__ First, voxel-wise regional probability maps were obtained using a Support Vector Machine classifier trained on local image features. Then the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints.  \ud
__Results__ The method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74:1%+-4:3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intra-class correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC = 0:86 between the volumes measured in scans repeated within a short time interval.  \ud
__Conclusions__ In this work a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps
On EM Reconstruction of a Multi Channel Shielded Applicator for Cervical
  Cancer Brachytherapy: A Feasibility Study

Electromagnetic tracking (EMT) is a promising technology for automated
catheter and applicator reconstruc- 10 tions in brachytherapy. In this work, a
proof-of-concept is presented for reconstruction of the individual channels of
a shielded tandem applicator dedicated to intensity modulated brachytherapy.
All six channels of a straight prototype was reconstructed and the distance
between two opposite channels was measured. A study was also conducted on the
influence of the shield on the data fluctuation of the EMT system. The
differences with the CAD specified dimensions are under 2 mm. The pair of
channels which has one of it more distant from the generator have 15 higher
inter-channel distance with higher variability. In the first 110 cm
reconstruction, all inter-channel distances are within the geometrical
tolerances. According to a paired Student t-test, the data given by the EM
system with and without the shield applicator tip are not significantly
different. This study shows that the reconstruction of channel path within the
mechanical accuracy of the applicator is possible.Comment: 3 pages, 3 figure
Using Stable Distributions to Characterize Proton Pencil Beams

Purpose: To introduce and evaluate the use of stable distributions as a means
of describing the behavior of charged particle pencil beams in a medium, with
specific emphasis on proton beam scanning (PBS). Methods: The proton pencil
beams of a clinically commissioned proton treatment facility are replicated in
a Monte Carlo simulation system (FLUKA). For each available energy the beam
deposition in water medium is characterized by the dose deposition. Using an
alpha--stable distribution methodology each beam with a nominal energy $E$ is
characterized by the lateral spread at depth $z$: $S(z;\alpha,\gamma,E)$ and a
total energy deposition $I_D(z)$. The beams are then described as a function of
the variation of the parameters at depth. Finally, an implementation in a
freely available open source dose calculation suite (matRad, DKFZ, Heidelberg,
Germany) is proposed. Results: Quantitatively, the fit of the stable
distributions, compared to those implemented in standard treatment planning
systems, are equivalent. The efficiency of the representation is better (2
compared to 3 and more parameters needed). The meta--parametrization (i.e. the
description of the dose deposition by only providing the fitted parameters)
allows for interpolation of non--measured data. In the case of the clinical
data used in this paper, it was possible to only commission 1 out of 5 nominal
energies to obtain a viable data set. Conclusions: Alpha--stable distributions
are intrinsically suited to describe charged particle pencil beams in a medium
and can be easily implemented in existing treatment planning systems. The use
of alpha-distributions can easily be extended to other particles
Dosimetric evidence confirms computational model for magnetic field
  induced dose distortions of therapeutic proton beams

Given the sensitivity of proton therapy to anatomical variations, this cancer
treatment modality is expected to benefit greatly from integration with
magnetic resonance (MR) imaging. One of the obstacles hindering such an
integration are strong magnetic field induced dose distortions. These have been
predicted in simulation studies, but no experimental validation has been
performed so far. Here we show the first measurement of planar distributions of
dose deposited by therapeutic proton pencil beams traversing a one-Tesla
transversal magnetic field while depositing energy in a tissue-like phantom
using film dosimetry. The lateral beam deflection ranges from one millimeter to
one centimeter for 80 to 180 MeV beams. Simulated and measured deflection agree
within one millimeter for all studied energies. These results proof that the
magnetic field induced proton beam deflection is both measurable and accurately
predictable. This demonstrates the feasibility of accurate dose measurement and
hence validates dose predictions for the framework of MR-integrated proton
therapy
Image-domain multi-material decomposition for dual-energy CT based on
  correlation and sparsity of material images

Dual energy CT (DECT) enhances tissue characterization because it can produce
images of basis materials such as soft-tissue and bone. DECT is of great
interest in applications to medical imaging, security inspection and
nondestructive testing. Theoretically, two materials with different linear
attenuation coefficients can be accurately reconstructed using DECT technique.
However, the ability to reconstruct three or more basis materials is clinically
and industrially important. Under the assumption that there are at most three
materials in each pixel, there are a few methods that estimate multiple
material images from DECT measurements by enforcing sum-to-one and a box
constraint ([0 1]) derived from both the volume and mass conservation
assumption. The recently proposed image-domain multi-material decomposition
(MMD) method introduces edge-preserving regularization for each material image
which neglects the relations among material images, and enforced the assumption
that there are at most three materials in each pixel using a time-consuming
loop over all possible material-triplet in each iteration of optimizing its
cost function. We propose a new image-domain MMD method for DECT that considers
the prior information that different material images have common edges and
encourages sparsity of material composition in each pixel using regularization
Low Rank Magnetic Resonance Fingerprinting

Purpose: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach
that provides quantitative MRI measures using randomized acquisition.
Extraction of physical quantitative tissue parameters is performed off-line,
without the need of patient presence, based on acquisition with varying
parameters and a dictionary generated according to the Bloch equation
simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for
acquisition, and therefore a high undersampling ratio in the sampling domain
(k-space) is required for reasonable scanning time. This undersampling causes
spatial artifacts that hamper the ability to accurately estimate the tissue's
quantitative values. In this work, we introduce a new approach for quantitative
MRI using MRF, called magnetic resonance Fingerprinting with LOw Rank (FLOR).
  Methods: We exploit the low rank property of the concatenated temporal
imaging contrasts, on top of the fact that the MRF signal is sparsely
represented in the generated dictionary domain. We present an iterative scheme
that consists of a gradient step followed by a low rank projection using the
singular value decomposition.
  Results: Experimental results consist of retrospective sampling, that allows
comparison to a well defined reference, and prospective sampling that shows the
performance of FLOR for a real-data sampling scenario. Both experiments
demonstrate improved parameter accuracy compared to other compressed-sensing
and low-rank based methods for MRF at 5% and 9% sampling ratios, for the
retrospective and prospective experiments, respectively.
  Conclusions: We have shown through retrospective and prospective experiments
that by exploiting the low rank nature of the MRF signal, FLOR recovers the MRF
temporal undersampled images and provides more accurate parameter maps compared
to previous iterative methods.Comment: 11 pages, 11 figure
RECORDS: Improved reporting of Monte Carlo radiation transport studies: Report of the AAPM Research Committee Task Group 268

© 2017 American Association of Physicists in Medicine. Studies involving Monte Carlo simulations are common in both diagnostic and therapy medical physics research, as well as other fields of basic and applied science. As with all experimental studies, the conditions and parameters used for Monte Carlo simulations impact their scope, validity, limitations, and generalizability. Unfortunately, many published peer-reviewed articles involving Monte Carlo simulations do not provide the level of detail needed for the reader to be able to properly assess the quality of the simulations. The American Association of Physicists in Medicine Task Group #268 developed guidelines to improve reporting of Monte Carlo studies in medical physics research. By following these guidelines, manuscripts submitted for peer-review will include a level of relevant detail that will increase the transparency, the ability to reproduce results, and the overall scientific value of these studies. The guidelines include a checklist of the items that should be included in the Methods, Results, and Discussion sections of manuscripts submitted for peer-review. These guidelines do not attempt to replace the journal reviewer, but rather to be a tool during the writing and review process. Given the varied nature of Monte Carlo studies, it is up to the authors and the reviewers to use this checklist appropriately, being conscious of how the different items apply to each particular scenario. It is envisioned that this list will be useful both for authors and for reviewers, to help ensure the adequate description of Monte Carlo studies in the medical physics literature.Postprint (published version
Acute leukemia in association with Langerhans cell histiocytosis

Langerhans cell histiocytosis (LCH) and malignancy occurring in the same individual is unusual and has generally been the subject of isolated case reports. To better define the occurrence of these events a registry of cases with synchronous or asynchronous LCH and malignancy was developed with the cooperation of the Histiocyte Society.\ud
\ud
In 1991 the Histiocyte Society surveyed its members requesting information on cases in which LCH was associated with malignancy. The questionnaire was mailed to all members of the society and specifically requested information on the clinical and laboratory features of the cases, disease evolution, and response to therapy. Retrospective reporting was allowed. With this initial data, an ongoing registry of LCH patients with associated malignancy was begun of such cases, including evolution and response to therapy.\ud
\ud
Twenty-seven patients were enrolled during the first year of the registry, of whom 4 patients had the association of LCH with a malignant lymphoma and 10 cases had an association of LCH with other types of solid tumor. The remaining 13 patients had the association of LCH with acute leukemia. In five cases, LCH was associated with acute lymphoblastic leukemia FAB L1 (ALL). In four cases the ALL preceded the LCH by 6 months to 1 year. In four of five patients the LCH was localized; in two instances the LCH was treated with chemotherapy. In all cases the leukemia was treated according to local standard ALL protocols and in one case autologous bone marrow transplantation (ABMT) was performed at relapse. Three patients are free of leukemia, one of whom has persistent localized LCH of the skin. Two patients died of the ALL, one of whom was free of the LCH at the time of death. In eight instances LCH was reported in association with acute myeloid leukemia (AML). Six of these patients had a generalized form of LCH. In seven the diagnosis of LCH preceded the diagnosis of leukemia by more than 2 years (median 4 years). In the remaining patient both diagnoses were made concurrently. In all seven cases in whom LCH was the initial diagnosis the treatment consisted of chemotherapy and/or radiotherapy. Seven patients died from the AML, five without evidence of LCH.\ud
\ud
The temporal patterns of the LCH-ALL and LCH-AML associations are distinct with ALL usually preceding the diagnosis of LCH and AML succeeding it. Such a pattern is suggestive that in cases of ALL the LCH may be a reactive process while in cases of AML occurring after LCH the primary LCH therapy may play an inductive role in the leukemia. © 1994 Wiley-Liss, Inc