OSCR

In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

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

Markdown · 45 lines · 4.1 KB · no license

  1. # chi-separation toolbox (*χ*-separation, x-separation)
  3. * ⭐ Toolbox version 1.2.2 is released!
  4. * ⭐ A download link for Toolbox version 1.2.2 will be sent to your email once you submit the Google Form: https://forms.gle/nhJahF86zpMgEKvM9
  5. * ⭐ This toolbox is developed for in-vivo human 3T and 7T datasets. Other field stengths, high resolution ex-vivo or nonhuman datasets (< 0.6 mm) are not fully tested and may require additional processing.
  6. * ⭐ E-mail us if you need data processing other than in-vivo human 3T datasets. We may be able to help you with scan protocol and processing.
  7. * ⭐ If you have both GRE and SE data, you have data processing options of conventional optimization (MEDI, iLSQR) and neural network (chi-sepnet-R2', better quality).
  8. * ⭐ If you only have GRE data, neural network (chi-sepnet-R2*) will deliver high quality *&chi;*-separation maps.
  9. * ⭐ Neural networks can process resolution > 0.6 mm. This makes high resolution ex-vivo or rodent data processing difficult if you only have GRE data.
  11. * The *&chi;*-separation toolbox includes the following features:
  12. - DICOM/NIFTI data compatibility
  13. - **QSMnet**: Quantitative susceptibility mapping (QSM) reconstruction algorithm based on deep neural network (QSMnet; J. Yoon et al., Neuroimage, 2018)
  14. - **_&chi;_-separation using _R_<sub>2</sub>' (or _R_<sub>2</sub>\* )**: Magnetic susceptibility source separation algorithms based on convex optimization (*&chi;*-separation; H. Shin et al., Neuroimage, 2021) that share similar contrasts and optimization parameters with either MEDI+0 (Liu et al., MRM, 2018) or iLSQR (Li et al., Neuroimage, 2015) algorithms. The toolbox also provides the option to use pseudo *R*<sub>2</sub> map if *R*<sub>2</sub> measurement is not availabe (using *R*<sub>2</sub>' is reconmmanded for accurate estimation).
  15. - **_&chi;_-sepnet using _R_<sub>2</sub>' (or _R_<sub>2</sub>\* )**: A U-Net-based neural network that reconstructs COSMOS-quality *&chi;*-separation using *R*<sub>2</sub>' and phase. In case *R*<sub>2</sub> is not measured, another neural network is trained to estimate *&chi;*-separation maps from *R*<sub>2</sub>\* and phase.
  16. - The toolbox also supports phase preprocessing (e.g. phase unwrapping and background removal) powered by MEDI, STI Suite, SEGUE, and mritools toolboxs (see the Chisep_script.m file for details).
  18. * Last update: June-03-2026 (Taechang Kim, Hyeong-Geol Shin)
  20. ## Requirements
  21. * MATLAB version (tested in R2021a-R2026a. Toolbox will fail any version before R2021a.)
  22. * MATLAB Add-Ons:
  23. - Image Processing Toolbox, Signal Processing Toolbox, Parallel Computing Toolbox, Statistics and Machine Learning Toolbox, Deep Learning Toolbox
  24. - For QSMnet and *&chi;*-sepnet, Deep Learning MATLAB Toolbox Converter for ONNX Model Format (https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format)
  26. ## Recommendation for acquisition
  27. * Multiecho GRE data for in-vivo
  28. - TR = 33 ms; TE1 = 5 ms; Echo spacing = 6 ms; Number of echoes = 5; flip angle = 15° Matrix size (AP LR HF) = 256 x 176 x 144 Resolution = isotropic 1 mm Parallel imaging factor = 2; elliptical k-space shutter Acquisition time = 6 mins.
  29. - Follow QSM consensus paper (https://doi.org/10.1002/mrm.30006)
  30. * Multiecho SE data for in-vivo
  31. - TBA
  32. * Multiecho GRE data for high resolution ex-vivo
  33. - Highly recommend to acquire multi-orientation data (relative to B0).
  35. ## Recommendation for data analysis
  36. * *&chi;*-separation template and regions of interest are available here [[link](https://github.com/SNU-LIST/chi-separation-atlas)]
  37. * K Min et al. A human brain atlas of *&chi;*-separation for normative iron and myelin distributions. NMR Biomed, 2024 online [[link](https://doi.org/10.1002/nbm.5226)]
  39. ## Reference
  40. * H. Shin, J. Lee, Y. H. Yun, S. H. Yoo, J. Jang, S.-H. Oh, Y. Nam, S. Jung, S. Kim, F. Masaki, W. Kim, H. J. Choi, J. Lee. *&chi;*-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage, 2021 Oct; 240:118371.
  42. ## Contacts
  43. * [email hidden]
  44. * [email hidden] (Hyeong-Geol Shin, PhD)
  45. * [email hidden] (Taechang Kim, PhD student @ SNU-LIST)

README.md at commit 29f901c, no license · at the source

Overview

Authors: Hyeong-Geol Shin1,2, Kelly A Mills2, Ted M Dawson2,3,4,5, Taechang Kim6, Jongho Lee6, Xu Li1,7, Peter van Zijl1,7, Jannik Prasuhn1,2,8,9,10
  1. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD USA
  2. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD USA
  3. Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA
  4. Department of Physiology, Pharmacology and Therapeutics, Johns Hopkins University School of Medicine, Baltimore, MD USA
  5. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD USA
  6. Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
  7. Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD USA
  8. Section of Movement Disorders, Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
  9. Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
  10. Center for Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
Institutions: Kennedy Krieger Institute (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); Seoul National University (South Korea); University of Lübeck (Germany)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 200
Dates: received 2 November 2025; accepted 29 April 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01384-x · PMID 42204172 · PMCID PMC13493794 · OpenAlex W7162552902
Open access: gold, a free copy (OpenAlex)
Status: empty repository
Categories: structural MRI / diffusion (modality), human (organism), Parkinson's (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Machine learning, fMRI & imaging
Keywords: Computational biology and bioinformatics, Diseases, Neurology, Neuroscience
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: National Institutes of Health (P41EB031771); NIH HHS (P41EB031771); NIBIB NIH HHS (P41 EB031771); Thiemann Stiftung
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Parkinson’s disease (PD) involves pathological iron accumulation, yet MRI metrics, such as R2* or magnetic susceptibility (χ), lack mechanistic specificity because they convolve paramagnetic and diamagnetic sources. We applied an AI-assisted χ-separation framework that combines deep learning (DL)-based preprocessing with biophysical modeling to assess paramagnetic iron with enhanced specificity. Twenty-five PD patients and twenty-six matched controls underwent 3 T multi-parametric MRI. DL-based χ-separation (χ-separationDL) separated the paramagnetic susceptibility component (χpara; indicative of iron) from χ, revealing alterations undetected by established susceptibility-based methods: χpara increased in dorsal premotor cortex ( +6.3%, P = 0.032) and substantia nigra pars compacta ( +10.2%, P = 0.024), χpara in premotor cortex correlated with disease duration (r = 0.41; P = 0.045). DL-based preprocessing was not inferior for the differentiation between PD patients vs. controls compared to established optimization-based χ-separation, indicating the potential for AI-enhanced χ-separation to be applied within the scope of susceptibility imaging in PD.

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

Repository

Its files are read in the Code ↔ Paper reader above.

SNU-LIST/chi-separation

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 29f901c39c70cf71eca4ba46cd8da2a67d16d874, 3 June 2026
Size: 1 file, 0 scripts
Software Heritage: not archived
Found in: the text, “MRI postprocessing and analyses”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
1 file
At the source: github.com/SNU-LIST/chi-separation

Code availability

The code used for the analyses in this study is available from the corresponding author upon reasonable request.

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

The datasets generated and/or analyzed during the current study are not publicly available due to data protection regulations and institutional ethics restrictions, as the data contain potentially identifiable clinical and imaging information. However, the datasets are available from the corresponding author on reasonable request and subject to approval by the responsible ethics committee and data transfer agreements in accordance with applicable data protection laws (including GDPR).

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 4 funders, 52 references.

Cite

This paper

Shin, H.-G., Mills, K. A., Dawson, T. M., Kim, T., Lee, J., Li, X., van Zijl, P., & Prasuhn, J. (2026). In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI. NPJ Parkinson's disease, 12(1), 200. https://doi.org/10.1038/s41531-026-01384-x

BibTeX

@article{shin2026vivo,
author = {Shin, Hyeong-Geol and Mills, Kelly A and Dawson, Ted M and Kim, Taechang and Lee, Jongho and Li, Xu and van Zijl, Peter and Prasuhn, Jannik},
title = {{In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = may,
volume = {12},
number = {1},
pages = {200},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01384-x},
url = {https://doi.org/10.1038/s41531-026-01384-x},
pmid = {42204172},
pmcid = {PMC13493794}
}

RIS

TY - JOUR
AU - Shin, Hyeong-Geol
AU - Mills, Kelly A
AU - Dawson, Ted M
AU - Kim, Taechang
AU - Lee, Jongho
AU - Li, Xu
AU - van Zijl, Peter
AU - Prasuhn, Jannik
TI - In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/05/27
VL - 12
IS - 1
SP - 200
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01384-x
UR - https://doi.org/10.1038/s41531-026-01384-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41531-026-01384-x",
"type": "article-journal",
"title": "In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI",
"container-title": "NPJ Parkinson's disease",
"author": [
{
"family": "Shin",
"given": "Hyeong-Geol"
},
{
"family": "Mills",
"given": "Kelly A"
},
{
"family": "Dawson",
"given": "Ted M"
},
{
"family": "Kim",
"given": "Taechang"
},
{
"family": "Lee",
"given": "Jongho"
},
{
"family": "Li",
"given": "Xu"
},
{
"family": "van Zijl",
"given": "Peter"
},
{
"family": "Prasuhn",
"given": "Jannik"
}
],
"container-title-short": "NPJ Parkinsons Dis",
"volume": "12",
"issue": "1",
"page": "200",
"DOI": "10.1038/s41531-026-01384-x",
"PMID": "42204172",
"PMCID": "PMC13493794",
"ISSN": "2373-8057",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41531-026-01384-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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:10.1002/nbm.70349 [code]
Susceptibility Source Separation Unveils Paramagnetic and Diamagnetic Trajectories in Healthy Brains From 5 to 90 Years.
Journal: NMR in biomedicine
In common: structural MRI / diffusion, 4 references
[2] doi:10.1038/s41531-026-01428-2 [code]
The degenerome-a novel streamline-wise approach for white matter integrity in neurodegeneration.
Journal: NPJ Parkinson's disease
In common: Parkinson's, structural MRI / diffusion, clinical / translational, 3 references
[3] doi:10.1371/journal.pone.0348957 [code]
Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.
Journal: PloS one
In common: Parkinson's, clinical / translational, 4 references
[4] doi:10.1002/mds.70355 [code]
Gray Matter Microstructure Measured Using Diffusion Imaging as a Biomarker of Severity in Lewy Body Diseases.
Journal: Movement disorders : official journal of the Movement Disorder Society
In common: Parkinson's, structural MRI / diffusion, clinical / translational, 3 references
[5] doi:10.1002/mrm.70468 [code]
A Realistic In Silico Brain Phantom for Quantifying Susceptibility Anisotropy-Induced Error in Susceptibility Separation.
Journal: Magnetic resonance in medicine
In common: structural MRI / diffusion, 3 references
[6] doi:10.1002/nbm.70301 [code]
Reducing Variability in Deep Gray Matter QSM Using Differential ROI Referencing: A Phantom and In Vivo Evaluation.
Journal: NMR in biomedicine
In common: Parkinson's, structural MRI / diffusion, 3 references
[7] doi:10.1038/s43856-026-01606-6 [code]
Validation of remote multimodal AI screening for Parkinson disease across diverse settings.
Journal: Communications medicine
In common: Parkinson's, clinical / translational, 3 references
[8] doi:10.1016/j.isci.2026.117185
Impact of Amazonian dance on speech performance in people with Parkinson's disease.
Journal: iScience
In common: Parkinson's, 3 references
[9] doi:10.1002/mrm.70366 [code]
Mesoscale Whole-Brain T&lt;sub&gt;2&lt;/sub&gt;*-Weighted and Associated Quantitative MRI in Humans at 10.5 T.
Journal: Magnetic resonance in medicine
In common: structural MRI / diffusion, 2 references
[10] doi:10.1038/s41531-026-01360-5 [code]
Hippocampal atrophy in untreated de novo Parkinson's disease with obstructive sleep apnea.
Journal: NPJ Parkinson's disease
In common: Parkinson's, structural MRI / diffusion, clinical / translational, 2 references

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.