OSCR

Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Pre‐Processing and Analysis ↔ BRC_diffusion_pipeline/dMRI_preproc.sh, lines 30–90 · score 0.70 · probabilistic tractography, susceptibility induced, BedpostX, motion correction, parallel, orientation
  2. [2] § Results › Denoising Comparisons ↔ BRC_diffusion_pipeline/scripts/brc_bedpostx.sh, lines 28–103 · score 0.65 · multi shell, BedpostX, ARD, ball, sticks, orientation
  3. [3] § Methods › Pre‐Processing and Analysis ↔ BRC_diffusion_pipeline/scripts/eddy_postproc.sh, lines 147–226 · score 0.55 · DTI V1, S0, FA, Post, eddy, FSL
  4. [4] § Results › Offline Reconstruction Features and Validation ↔ BRC_functional_pipeline/scripts/EPI_Distortion_Correction.sh, lines 39–112 · score 0.51 · blip reversed, phase encoding, scans, pipeline
  5. [5] § Results › Offline Reconstruction Features and Validation ↔ BRC_functional_pipeline/scripts/EPI_Distortion_Correction.sh, lines 39–112 · score 0.51 · blip reversed, phase encoding, scans, pipeline
  6. [6] § Methods › Image Reconstruction Pipeline ↔ BRC_functional_pipeline/scripts/EPI_Distortion_Correction.sh, lines 39–112 · score 0.50 · spin echo, phase encoding, fieldmap, distortions, Pipeline
  7. [7] § Methods › Image Reconstruction Pipeline ↔ BRC_functional_pipeline/scripts/EPI_Distortion_Correction.sh, lines 39–112 · score 0.50 · spin echo, phase encoding, fieldmap, distortions, Pipeline

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Shell · 112 lines · 3.6 KB · no license · 2 matches

  1. #!/bin/bash
  2. # Last update: 01/10/2018
  4. # Authors: Ali-Reza Mohammadi-Nejad, & Stamatios N Sotiropoulos
  5. #
  6. # Copyright 2018 University of Nottingham
  7. #
  8. set -e
  10. source $BRC_GLOBAL_SCR/log.shlib # Logging related functions
  12. # ---------------------------------------------------------------------
  13. # Constants for specification of Readout Distortion Correction Method
  14. # ---------------------------------------------------------------------
  16. FIELDMAP_METHOD_OPT="FIELDMAP"
  17. SIEMENS_METHOD_OPT="SiemensFieldMap"
  18. GENERAL_ELECTRIC_METHOD_OPT="GeneralElectricFieldMap"
  19. SPIN_ECHO_METHOD_OPT="TOPUP"
  21. # function for parsing options
  22. getopt1()
  23. {
  24. local sopt="$1"
  25. shift 1
  26. local fn
  27. for fn in "$@" ; do
  28. case "$fn" in
  29. "${sopt}"=*) printf '%s\n' "${fn#*=}"; return 0 ;;
  30. esac
  31. done
  32. }
  34. defaultopt()
  35. {
  36. echo $1
  37. }
  39. # parse arguments
  40. WD=`getopt1 "--workingdir" $@`
  41. topupFolderName=`getopt1 "--topupfoldername" $@`
  42. ScoutInputName=`getopt1 "--scoutin" $@`
  43. SpinEchoPhaseEncodeNegative=`getopt1 "--SEPhaseNeg" $@`
  44. SpinEchoPhaseEncodePositive=`getopt1 "--SEPhasePos" $@`
  45. EchoSpacing=`getopt1 "--echospacing" $@`
  46. UnwarpDir=`getopt1 "--unwarpdir" $@`
  47. TopupConfig=`getopt1 "--topupconfig" $@`
  48. GradientDistortionCoeffs=`getopt1 "--gdcoeffs" $@`
  49. DistortionCorrection=`getopt1 "--method" $@`
  50. LogFile=`getopt1 "--logfile" $@`
  52. log_SetPath "${LogFile}"
  54. log_Msg 2 "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
  55. log_Msg 2 "WD:$WD"
  56. log_Msg 2 "topupFolderName:$topupFolderName"
  57. log_Msg 2 "ScoutInputName:$ScoutInputName"
  58. log_Msg 2 "SpinEchoPhaseEncodeNegative:$SpinEchoPhaseEncodeNegative"
  59. log_Msg 2 "SpinEchoPhaseEncodePositive:$SpinEchoPhaseEncodePositive"
  60. log_Msg 2 "EchoSpacing:$EchoSpacing"
  61. log_Msg 2 "UnwarpDir:$UnwarpDir"
  62. log_Msg 2 "TopupConfig:$TopupConfig"
  63. log_Msg 2 "GradientDistortionCoeffs:$GradientDistortionCoeffs"
  64. log_Msg 2 "DistortionCorrection:$DistortionCorrection"
  65. log_Msg 2 "LogFile:$LogFile"
  66. log_Msg 2 "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
  69. TopupConfig=`defaultopt $TopupConfig ${BRC_GLOBAL_DIR}/config/b02b0.cnf.txt`
  70. ########################################## DO WORK ##########################################
  72. case $DistortionCorrection in
  74. ${FIELDMAP_METHOD_OPT} | ${SIEMENS_METHOD_OPT} | ${GENERAL_ELECTRIC_METHOD_OPT})
  75. ;;
  78. ${SPIN_ECHO_METHOD_OPT})
  80. # Use topup to distortion correct the scout scans using a blip-reversed SE pair "fieldmap" sequence
  82. ${BRC_FMRI_SCR}/TopupPreprocessing.sh \
  83. --workingdir=${WD}/${topupFolderName} \
  84. --scoutin=${ScoutInputName} \
  85. --phaseone=${SpinEchoPhaseEncodeNegative} \
  86. --phasetwo=${SpinEchoPhaseEncodePositive} \
  87. --echospacing=${EchoSpacing} \
  88. --unwarpdir=${UnwarpDir} \
  89. --topupconfig=${TopupConfig} \
  90. --gdcoeffs=${GradientDistortionCoeffs} \
  91. --outfolder=${WD} \
  92. --owarp=${WD}/WarpField \
  93. --ojacobian=${WD}/Jacobian \
  94. --logfile=${LogFile}
  96. ;;
  99. *)
  100. log_Msg 3 "UNKNOWN DISTORTION CORRECTION METHOD: ${DistortionCorrection}"
  101. exit 1
  102. esac
  104. log_Msg 3 ""
  105. log_Msg 3 " END: EPI Distortion Correction"
  106. log_Msg 3 " END: `date`"
  107. log_Msg 3 "=========================================================================="
  108. log_Msg 3 " =============== "
  110. ################################################################################################
  111. ## Cleanup
  112. ################################################################################################

EPI_Distortion_Correction.sh at commit 7da12b1, no license · at the source

Overview

Authors: Francesco D'Antonio1,2, Shaun Warrington1,2, Jose‐Pedro Manzano‐Patron1,2, Paul S. Morgan1,2,3, Stamatios N. Sotiropoulos1,2,3
ORCID iDs: Francesco D'Antonio, Shaun Warrington, Jose‐Pedro Manzano‐Patron, Paul S. Morgan, Stamatios N. Sotiropoulos
  1. Sir Peter Mansfield Imaging Centre, School of Medicine The University of Nottingham Nottingham UK
  2. Mental Health and Clinical Neurosciences, School of Medicine The University of Nottingham Nottingham UK
  3. National Institute of Health and Care Research Nottingham Biomedical Research Centre, Nottingham University Hospitals Nottingham UK
Institutions: University of Nottingham (United Kingdom); Nottingham University Hospitals NHS Trust (United Kingdom); Nottingham Biomedical Research Centre (United Kingdom)
Journal: Magnetic resonance in medicine, volume 96, issue 1, pages 435-447
Dates: received 6 November 2025; accepted 25 February 2026; published online 7 March 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70336 · PMID 41794999 · PMCID PMC13156438 · OpenAlex W7134189495
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: AIR‐recon DL, denoising, diffusion MRI, image reconstruction, MPPCA, NORDIC
MeSH: Brain*, Diffusion Magnetic Resonance Imaging*, Image Processing, Computer-Assisted*, Algorithms, Artificial Intelligence, Convolutional Neural Networks, Humans, Principal Component Analysis, Reproducibility of Results, Signal-To-Noise Ratio, Software, White Matter (* major topic)
Journal subjects: Computer Processing and Modeling
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: GE Healthcare; University of Nottingham; European Research Council (101000969)
Citations: cited by 1 paper (Europe PMC); 54 references in the paper

Abstract

Purpose: Optimal diffusion MRI (dMRI) data for image denoising is often unavailable from scanner reconstruction. In this work, we make available an offline reconstruction pipeline for GE dMRI acquisitions, giving access to complex dMRI data. Furthermore, we compare the efficacy of GE HealthCare's AIR‐Recon DL (ARDL), a proprietary convolutional neural network‐based reconstruction and denoising approach, to patch‐based MPPCASVS and NORDIC denoising methods on high‐resolution dMRI data.

Methods: We developed an end‐to‐end offline dMRI reconstruction pipeline for GE HealthCare acquisitions, augmenting the Orchestra software development kit, and validated its output against scanner reconstruction. We used it to compare MPPCASVS, NORDIC, and ARDL denoising approaches, considering underlying metrics reflecting noise variance and bias, such as the signal profiles in highly anisotropic areas, and secondary downstream measurements, such as fiber orientation estimation and white matter tractography.

Results: Our validated offline reconstruction supports various in‐plane/out‐of‐plane accelerations and partial Fourier reconstruction methods. Unlike scanner reconstruction, it provides access to complex dMRI data, enabling denoising in the complex domain, which demonstrated superior noise floor suppression compared with magnitude‐constrained denoising. PCA‐based denoising methods had improved spatial resolution, contrast‐to‐noise and more robust fiber orientation estimation compared with ARDL.

Conclusion: We found significant gains in dMRI data quality when using the proposed offline reconstruction pipeline, allowing complex‐domain denoising to obtain high‐quality data at high spatial resolution and b‐value, using a wide‐bore scanner and a standard PGSE EPI sequence. MPPCASVS and NORDIC (4D PCA‐based) outperformed ARDL (2D) in terms of spatial resolution and reduction of noise variance.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

weconnect.gehealthcare.com

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Image Reconstruction Pipeline”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: weconnect.gehealthcare.com/

SteenMoeller/NORDIC_Raw

License: other
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f580bd10d54aa64a257fe36ba3f088dde5997089, 29 September 2026
Languages: MATLAB (5)
Size: 7 files, 5 scripts
Software Heritage: not archived
Found in: the text, “Denoising Comparisons”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
7 files
At the source: github.com/SteenMoeller/NORDIC_Raw

Zenodo 3909526

License: other-open
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (49 files), Statistics and Machine Learning Toolbox (8 files), FreeSurfer (5 files), Tools for NIfTI and ANALYZE image (MATLAB) (5 files), NumPy (4 files), Optimization Toolbox (1 file), Signal Processing Toolbox (1 file), Matplotlib (1 file), NiBabel (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file), SPM (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
140 files
At the source:

spmic-uon/brc_pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7da12b175e83cfcdb374c6c9aa924c4f2a74a98d, 7 August 2026
Languages: Shell (101), MATLAB (58), JavaScript (12), Python (8), C (5), C/C++ (1)
Size: 405 files, 185 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, CITATION.cff
Not found: license file, environment file, tests, continuous integration, documentation
Tools: FSL (51 files), Statistics and Machine Learning Toolbox (8 files), NumPy (4 files), FreeSurfer (2 files), DIPY (1 file), Optimization Toolbox (1 file), Signal Processing Toolbox (1 file), Matplotlib (1 file), MRtrix3 (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
120 files
At the source: github.com/SPMIC-UoN/BRC_Pipeline/tree/v1.4.3

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 264 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The offline reconstruction pipeline is available through GE HealthCare's WeConnect website (https://weconnect.gehealthcare.com/) with the appropriate research license. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 12 MeSH terms, 3 funders, 48 references.

Cite

This paper

D'Antonio, F., Warrington, S., Manzano‐Patron, J., Morgan, P. S., & Sotiropoulos, S. N. (2026). Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising. Magnetic resonance in medicine, 96(1), 435-447. https://doi.org/10.1002/mrm.70336

BibTeX

@article{dantonio2026offline,
author = {D'Antonio, Francesco and Warrington, Shaun and Manzano‐Patron, Jose‐Pedro and Morgan, Paul S. and Sotiropoulos, Stamatios N.},
title = {{Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = mar,
volume = {96},
number = {1},
pages = {435--447},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70336},
url = {https://doi.org/10.1002/mrm.70336},
pmid = {41794999},
pmcid = {PMC13156438}
}

RIS

TY - JOUR
AU - D'Antonio, Francesco
AU - Warrington, Shaun
AU - Manzano‐Patron, Jose‐Pedro
AU - Morgan, Paul S.
AU - Sotiropoulos, Stamatios N.
TI - Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/03/07
VL - 96
IS - 1
SP - 435
EP - 447
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70336
UR - https://doi.org/10.1002/mrm.70336
LA - en
ER -

CSL-JSON

{
"id": "10.1002/mrm.70336",
"type": "article-journal",
"title": "Offline Reconstruction of Diffusion MRI Acquisitions for Comparison Between Complex PCA-Based and AI-Based Denoising",
"container-title": "Magnetic resonance in medicine",
"author": [
{
"family": "D'Antonio",
"given": "Francesco"
},
{
"family": "Warrington",
"given": "Shaun"
},
{
"family": "Manzano‐Patron",
"given": "Jose‐Pedro"
},
{
"family": "Morgan",
"given": "Paul S."
},
{
"family": "Sotiropoulos",
"given": "Stamatios N."
}
],
"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "1",
"page": "435-447",
"DOI": "10.1002/mrm.70336",
"PMID": "41794999",
"PMCID": "PMC13156438",
"ISSN": "0740-3194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/mrm.70336",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
7
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41586-026-10631-3 [code]
A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, Parallel Computing Toolbox, 12 other tools, 2 references, author Paul S Morgan
[2] doi:10.1038/s41467-026-71151-2 [code]
Common and distinct neural correlates of social interaction processing and theory of mind in narratives.
Journal: Nature communications
In common: MRtrix3, Optimization Toolbox, Parallel Computing Toolbox, 12 other tools
[3] doi:10.1002/nbm.70277 [code]
Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data.
Journal: NMR in biomedicine
In common: DIPY, Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, 7 other tools, structural MRI / diffusion, 4 references
[4] doi:10.1038/s41598-026-39162-7 [code]
White matter microstructure differences in obstructive sleep apnea severity groups assessed by diffusion tensor metrics and biophysical modeling.
Journal: Scientific reports
In common: DIPY, MRtrix3, NiBabel, 3 other tools, structural MRI / diffusion, 7 references
[5] doi:10.7554/elife.108408 [code]
Frequency and laminar profile of feature-specific visual activity revealed by interleaved EEG-fMRI.
Journal: eLife
In common: DIPY, Tools for NIfTI and ANALYZE image (MATLAB), FreeSurfer, 11 other tools
[6] doi:10.1038/s41467-026-73668-y [code]
Convergent and divergent brain-cognition development in early adolescence.
Journal: Nature communications
In common: MRtrix3, Tools for NIfTI and ANALYZE image (MATLAB), FreeSurfer, 11 other tools
[7] doi:10.1038/s41597-026-06616-6 [code]
Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI.
Journal: Scientific data
In common: MRtrix3, FreeSurfer, FSL, 3 other tools, methods / tools, structural MRI / diffusion, 6 references
[8] doi:10.1162/imag.a.1262 [code]
Frame-wise multi-echo distortion correction for superior functional MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, Parallel Computing Toolbox, 11 other tools
[9] doi:10.1002/hbm.70602 [code]
Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial.
Journal: Human brain mapping
In common: Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, Parallel Computing Toolbox, 11 other tools
[10] doi:10.1111/ene.70678 [code]
Who Falls After a Stroke? Evidence From a Prospective Stroke Cohort.
Journal: European journal of neurology
In common: Tools for NIfTI and ANALYZE image (MATLAB), Optimization Toolbox, Parallel Computing Toolbox, 11 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.