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A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI.

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Paper

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The authors' code

MATLAB · 100 lines · 3.9 KB · no license

  2. % ========================================================================
  3. % * Function: h1 = cmp_subpx_prof( mp2rage_i, mp2epiu_i, mp2epic_i, n_lines, n_sample )
  4. % - draw sub-pixel line profiles
  5. %
  6. % * Input :
  7. % --------------------------------------------------------------
  8. % | Description
  9. % --------------------------------------------------------------
  10. % mp2rage_i | MP2RAGE image (2D) (*.nii)
  11. % mp2epiu_i | MP2EPI-uncorrected image (2D) (*.nii)
  12. % mp2epic_i | MP2EPI-corrected image (2D) (*.nii)
  13. % n_lines | number of lines
  14. % n_sample | number of samples per line
  15. % --------------------------------------------------------------
  16. %
  17. % * Output :
  18. % --------------------------------------------------------------
  19. % | Description
  20. % --------------------------------------------------------------
  21. % hi | figure handle
  22. % --------------------------------------------------------------
  23. %
  24. % * Dev.: Seong Dae Yun ([email hidden])
  25. % * Ref.: Yun et al. A Novel Distortion-Matched Anatomical Imaging Sequence for
  26. % High-Fidelity Functional Mapping in Submillimeter-Resolution fMRI.
  27. % Sci Rep. 2026. doi: 10.1038/s41598-026-58377-2. in press.
  28. % ========================================================================
  31. function hi = cmp_subpx_prof( mp2rage_i, mp2epiu_i, mp2epic_i, n_lines, n_sample )
  34. % Get coordinates
  35. hi = figure ;
  36. imshow( mp2rage_i, [0 max( mp2rage_i(:) )*0.75] )
  37. [x_input, y_input] = ginput(2);
  39. % Get profiles
  40. del_xy = 0.05 ;
  41. prof_mp2rage = zeros( n_sample, n_lines ) ;
  42. prof_mp2epiu = zeros( n_sample, n_lines ) ;
  43. prof_mp2epic = zeros( n_sample, n_lines ) ;
  44. for ii = 1 : 1 : n_lines
  46. x_coords = x_input - floor( n_lines/2 )*del_xy + (ii-1)*del_xy ;
  47. y_coords = y_input - floor( n_lines/2 )*del_xy + (ii-1)*del_xy ;
  49. h1 = drawpolyline( gca, 'Position', [x_coords y_coords], 'Color', [1.0 1.0 0.0], 'LineWidth', 4 ) ;
  50. h1.MarkerSize = 0.01 ;
  51. h1.InteractionsAllowed = 'none' ;
  53. prof_mp2rage( :, ii ) = improfile( mp2rage_i, x_coords, y_coords, n_sample, 'bicubic' ) ;
  54. prof_mp2epiu( :, ii ) = improfile( mp2epiu_i, x_coords, y_coords, n_sample, 'bicubic' ) ;
  55. prof_mp2epic( :, ii ) = improfile( mp2epic_i, x_coords, y_coords, n_sample, 'bicubic' ) ;
  57. end
  59. % Mean +- std
  60. prof_a_mp2rage = mean( prof_mp2rage, 2 ) ;
  61. prof_s_mp2rage = std ( prof_mp2rage, 0, 2 ) ;
  62. prof_a_mp2epiu = mean( prof_mp2epiu, 2 ) ;
  63. prof_s_mp2epiu = std ( prof_mp2epiu, 0, 2 ) ;
  64. prof_a_mp2epic = mean( prof_mp2epic, 2 ) ;
  65. prof_s_mp2epic = std ( prof_mp2epic, 0, 2 ) ;
  70. % Display
  71. idx_GM_start_mp2rage = 30 ;
  72. idx_GM_start_mp2epiu = 52 ;
  73. idx_GM_start_mp2epic = 30 ;
  75. figure( 'Color', 'w' ) ;
  76. hold on ;
  77. grid on ;
  79. x_axis = linspace( 0, size( prof_mp2epic, 1 )-1, size( prof_mp2epic, 1 ) )' ;
  80. x_patch = [x_axis; flipud(x_axis)] ;
  81. y_patch_mp2rage = [prof_a_mp2rage + prof_s_mp2rage; flipud(prof_a_mp2rage - prof_s_mp2rage)];
  82. y_patch_mp2epiu = [prof_a_mp2epiu + prof_s_mp2epiu; flipud(prof_a_mp2epiu - prof_s_mp2epiu)];
  83. y_patch_mp2epic = [prof_a_mp2epic + prof_s_mp2epic; flipud(prof_a_mp2epic - prof_s_mp2epic)];
  85. fill( x_patch, y_patch_mp2rage, [0.5, 0.5, 0.5], 'EdgeColor', 'none', 'FaceAlpha', 0.4 ) ;
  86. plot( x_axis , prof_a_mp2rage, 'black', 'LineWidth', 2.5 ) ;
  88. ru = prof_a_mp2rage(idx_GM_start_mp2rage)/prof_a_mp2epiu(idx_GM_start_mp2epiu) ;
  89. fill( x_patch, y_patch_mp2epiu*ru, [0.8, 0.8, 1.0], 'EdgeColor', 'none', 'FaceAlpha', 0.4 ) ;
  90. plot( x_axis , prof_a_mp2epiu*ru, 'blue', 'LineWidth', 2.5 ) ;
  92. rc = prof_a_mp2rage(idx_GM_start_mp2rage)/prof_a_mp2epic(idx_GM_start_mp2epic) ;
  93. fill( x_patch, y_patch_mp2epic*rc, [1.0, 0.8, 0.8], 'EdgeColor', 'none', 'FaceAlpha', 0.4 ) ;
  94. plot( x_axis , prof_a_mp2epic*rc, 'red', 'LineWidth', 2.5 ) ;

cmp_subpx_prof.m at commit 3a0a37b, no license · at the source

Overview

Authors: Seong Dae Yun1, Patricia Pais-Roldán1, Jeongbeen Lee1,2, N. Jon Shah1,3,4,5
ORCID iDs: Seong Dae Yun
  1. Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich,52425 Jülich, Germany
  2. Department of Brain and Cognitive Sciences, Scranton College, Ewha Womans University,Seoul, Republic of Korea
  3. Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich,Jülich, Germany
  4. JARA - BRAIN - Translational Medicine, Aachen, Germany
  5. Department of Neurology, RWTH Aachen University,Aachen, Germany
Institutions: Forschungszentrum Jülich (Germany); Ewha Womans University (South Korea); RWTH Aachen University (Germany)
Journal: Scientific reports, volume 16, issue 1, article 19182
Dates: received 12 August 2025; accepted 12 June 2026; published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-58377-2 · PMID 42321384 · PMCID PMC13282486 · OpenAlex W7165368365
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Machine learning, fMRI & imaging
Keywords: Geometric distortion correction, Distortion-matched Anatomy, EPI, FMRI, Functional mapping accuracy, MP2RAGE, Anatomy, Biological techniques, Medical research, Neuroscience
MeSH: Brain*, Brain Mapping*, Echo-Planar Imaging*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Algorithms, Humans (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Forschungszentrum Jülich GmbH (4205)
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Echo-planar imaging (EPI), commonly employed in functional MRI (fMRI), is highly susceptible to magnetic field inhomogeneities, leading to pronounced geometric distortions in reconstructed images. These distortions can result in substantial structural discrepancies between EPI and anatomical images acquired using the magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) method, thereby making it challenging to achieve accurate co-registration and subsequent localization of functional mapping. This issue can be effectively addressed by employing an anatomical imaging sequence that exhibits distortion profiles identical to those in EPI, referred to here as MP2EPI (magnetization-prepared 2 EPI). While this approach enables effortless co-registration with functional scans, it also introduces geometric distortions into the anatomical reference imaging, which limits its utility for analyses that rely on morphometric measurements or atlas-based segmentation. Distortion in MP2EPI can be corrected using additional data acquired with the reversed phase-encoding (PE) direction, which, however, significantly increases total acquisition time. To overcome this limitation, this work presents a novel MP2EPI sequence that simultaneously acquires reversed PE data within a single MP2EPI acquisition, without increasing the overall scan time. The primary focus of the current work is the technical implementation and validation of this sequence in the context of submillimeter fMRI at 7T.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-58377-2.

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

Repository

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

SeongDaeYun/NovelMP2EPI

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3a0a37b8d98bcde2f4739cf42c5dfd06fef58f04, 15 June 2026
Languages: MATLAB (2), Shell (1)
Size: 4 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FreeSurfer (1 file), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/SeongDaeYun/NovelMP2EPI

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 3 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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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

This study presents human in vivo brain data, the sharing of which may raise concerns regarding the protection of personal data and privacy. The Council of Europe’s policy on data protection specifically applies to health-related data (CM/Rec(2019)2), and ethical guidelines and internal administrative documents further ensure the confidentiality of in vivo data and any metadata derived from the original data. Data sharing is available upon request to Prof. N. Jon Shah under a formal data-sharing agreement. Sharing is contingent on the consent of the subjects whose data are involved, and they will be informed in advance. The custom scripts used for image processing and analysis in this study are publicly available at: https://github.com/SeongDaeYun/NovelMP2EPI.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 keywords, 7 MeSH terms, 1 funder, 50 references.

Cite

This paper

Yun, S. D., Pais-Roldán, P., Lee, J., & Shah, N. J. (2026). A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI. Scientific reports, 16(1), 19182. https://doi.org/10.1038/s41598-026-58377-2

BibTeX

@article{yun2026novel,
author = {Yun, Seong Dae and Pais-Roldán, Patricia and Lee, Jeongbeen and Shah, N. Jon},
title = {{A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {19182},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58377-2},
url = {https://doi.org/10.1038/s41598-026-58377-2},
pmid = {42321384},
pmcid = {PMC13282486}
}

RIS

TY - JOUR
AU - Yun, Seong Dae
AU - Pais-Roldán, Patricia
AU - Lee, Jeongbeen
AU - Shah, N. Jon
TI - A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/19
VL - 16
IS - 1
SP - 19182
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58377-2
UR - https://doi.org/10.1038/s41598-026-58377-2
LA - en
ER -

CSL-JSON

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"author": [
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"given": "Seong Dae"
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"page": "19182",
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"PMCID": "PMC13282486",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-58377-2",
"language": "en",
"issued": {
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}
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