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Mesoscale Whole-Brain T<sub>2</sub>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › MRI Experiments ↔ ste/ste_final_prep_nintp.m, lines 2–91 · score 0.65 · sensitivity map, reference scan, GRE images, FOV, slice, channels
  2. [2] § Methods › Image Reconstruction ↔ ste/ste_final_prep_nintp.m, lines 2–91 · score 0.63 · sensitivity maps, reference scans, GRE images, reconstructed, channel, motion

Paper

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The authors' code

MATLAB · 189 lines · 7.1 KB · no license · 2 matches

  1. % 20230621: Jiaen Liu, normalize b1 data by the covariance matrix to improve sensitivity estimation, especially useful for 10.5T
  2. function [para,ste_info,b0_fit,mot_par_test,b1]=ste_final_prep_nintp(sorted_ste,ste_info,mask,mask_brain,db0,mot_f,...
  3. imf_uncomb,par)
  4. % prepare the parameters, sense and b0 maps to be used in reconstructing gre images
  5. para=sorted_ste.para;
  6. para.idx_shot_cenpf=sorted_ste.idx_shot_cenpf;
  7. % combine the field changes from full-fov and
  8. % accelerated images
  9. % Group motion parameters to clusters based on k-means
  10. ste_info=ste_field(ste_info,db0,...
  11. para,sorted_ste,par,mask_brain);
  12. if par.discard
  13. ste_info=ste_discard(ste_info,sorted_ste);
  14. end
  15. % coordinate of the ste image
  16. nx_ste=para.steref_dim_r;
  17. ny_ste=para.steref_dim_p;
  18. nz_ste=para.steref_dim_s;
  19. res_ste=[para.steref_res_r,...
  20. para.steref_res_p,...
  21. para.steref_res_s];
  22. x_ste=([1:nx_ste]-(1+nx_ste)/2)*res_ste(1);
  23. y_ste=([1:ny_ste]-(1+ny_ste)/2)*res_ste(2);
  24. z_ste=([1:nz_ste]-(1+nz_ste)/2)*res_ste(3);
  25. [x_ste,y_ste,z_ste]=ndgrid(x_ste,y_ste,z_ste);
  26. coord_ste=cat(4,x_ste,y_ste,z_ste);
  27. % coordiante of the main acquision
  28. nx=para.nr;
  29. ny=para.np;
  30. nz=para.n_slices*para.n_partitions;
  31. x=([1:nx]-(nx+1)/2)*para.resr;
  32. y=([1:ny]-(ny+1)/2)*para.resp;
  33. z=([1:nz]-(nz+1)/2)*para.ress;
  34. [x,y,z]=ndgrid(x,y,z);
  35. coord_main=cat(4,x,y,z);
  36. % calculate interpolation kernel for B0
  37. % which is always based on ste
  38. intp_ker_ste=mkl_interp_kernel(coord_ste,coord_main);
  39. % interpolation kernel for B1 depends on the use of
  40. % external reference
  41. intp_ker_ext=[];
  42. % ----------------------------------------------- %
  43. % Obtain sensitivity maps for each cluster
  44. % ----------------------------------------------- %
  45. isen=zeros(ste_info.nc,1);
  46. for i=1:ste_info.nc
  47. isen(i)=ste_info.md{i}.isen;
  48. end
  49. if field_true(par,'use_pri_b1')
  50. b1=interp3_nmat(coord_main,coord_ste,read_data(rp('sensit_pri.svd')));
  51. else
  52. if isfield(par,'use_ext_ref') && ...
  53. ~isempty(par.use_ext_ref) && ...
  54. par.use_ext_ref(1)>0
  55. % use sense reference from a seperate scan
  56. % par.use_ext_ref(1) is a mid number
  57. % par.use_ext_ref(2) is mask_threshold
  58. b1=repmat(ste_sense_ext(par,sorted_ste),[1,1,1,1,ste_info.nc]);
  59. % calculate interpolation kernel for B1
  60. % based on the external reference
  61. mid_pimg=par.use_ext_ref(1);
  62. para_pimg=getfield(sorted_ste.para_pimg,['mid' num2str(mid_pimg)]);
  63. coord_ext_magnet=get_coordinate(para_pimg,1,0,0);
  64. coord_main_magnet=get_coordinate(para,1,0,0);
  65. intp_ker_ext=mkl_interp_kernel(coord_ext_magnet,coord_main_magnet);
  66. else
  67. % use internal reference scan
  68. if isempty(imf_uncomb) || ...
  69. isempty(mot_f)
  70. error('*** No image data is provided for internal reference of B1! ***');
  71. end
  72. b1=ste_sense(imf_uncomb,mask,...
  73. [sorted_ste.te(1+floor(end/4)),...
  74. sorted_ste.te(1+end/2+floor(end/4))]*1e-3,...
  75. mot_f(:,isen),[],[],par,res_ste/par.ste_intp_res);
  76. end
  77. end
  78. mask_b1=squeeze(sum(abs(b1),4))>0;
  79. % need to interplate mask_b1 to ste
  80. if isfield(par,'use_ext_ref') && ...
  81. ~isempty(par.use_ext_ref) && ...
  82. par.use_ext_ref(1)>0
  83. coord_ste_magnet=get_coordinate(para,1,1,0);
  84. mask_b1=interp3_nmat(coord_ext_magnet,coord_ste_magnet,single(mask_b1))>0.5;
  85. end
  86. nch=para.n_channels;
  87. [nxb1,nyb1,nzb1,~,~]=size(b1);
  88. nb1=numel(b1)/nch/ste_info.nc;
  89. b1=reshape(b1,[nb1,nch,ste_info.nc]);
  90. b1=permute(b1,[1,3,2]);
  91. b1=reshape(b1,[nb1*ste_info.nc,nch]);
  93. if ~(isfield(par,'use_ext_ref') && ...
  94. ~isempty(par.use_ext_ref) && ...
  95. par.use_ext_ref(1)>0)
  96. % for external sense reference scan, this was already done and not needed here.
  97. % see ste_sense_ext.m for details
  98. b1=b1*conj(chol(sorted_ste.inv_cov,'lower'));
  99. end
  100. b1=reshape(b1,[nb1,ste_info.nc,nch]);
  101. b1=permute(b1,[1,3,2]);
  102. b1=reshape(b1,[nxb1,nyb1,nzb1,nch,ste_info.nc]);
  103. b1=b1/prctile(abs(b1(:)),95);
  104. para.b1n=b1;
  105. % ----------------------------------------------- %
  106. % B0 maps for each cluster
  107. % ----------------------------------------------- %
  108. b0=zeros(nx_ste,ny_ste,nz_ste,ste_info.nc);
  109. mask_ext=zeros(nx_ste,ny_ste,nz_ste);
  110. mask_brain_ext=zeros(nx_ste,ny_ste,nz_ste);
  111. % shrink mask_brain
  112. mask_brain=volerode(mask_brain,2);
  113. kernel = fspecial('gaussian',7,1);
  114. for i = 1:nz_ste
  115. mask_ext(:, :, i) = imfilter(double(mask(:, :, i)), kernel);
  116. mask_brain_ext(:,:,i)=imfilter(double(mask_brain(:, :, i)), kernel);
  117. end
  118. % $$$ mask_b1_ext=zeros(nx_ste,ny_ste,nz_ste,ste_info.nc);
  119. % $$$ for j=1:size(mask_b1,4)
  120. % $$$ for i=1:nz_ste
  121. % $$$ mask_b1
  122. % $$$ end
  123. % $$$ end
  124. mask_ext(find(mask)) = 1.0;
  125. Am=gen_spher_harm_poly(x_ste,y_ste,z_ste,par.ord);
  128. for i=1:ste_info.nc
  129. b0(:,:,:,i)=reshape(Am*ste_info.md{i}.c_db0,...
  130. [nx_ste,ny_ste,nz_ste]);
  131. end
  132. % b0=b0.*mask_ext;
  133. b0=b0.*mask_b1;
  135. % $$$ b0_meas=zeros(nx_ste,ny_ste,nz_ste,ste_info.nc);
  136. % $$$ for i=1:ste_info.nc
  137. % $$$ b0_meas(:,:,:,i)=ste_info.md{i}.b0;
  138. % $$$ end
  139. % combine measured and fitted b0
  140. % b0=b0.*(1-mask_brain_ext)+b0_meas.*mask_brain_ext;
  141. % b0=b0.*mask_main_ext;
  143. b0_fit=zeros(size(db0));
  144. b0_fit=combine_dim(b0_fit,[4,5]);
  145. nshot=sorted_ste.nshot;
  146. idx_gre=ste_info.idx_gre;
  147. idx=ste_info.idx(1:length(sorted_ste.idx_shot_cenpf));
  148. db0tmp=zeros(nshot,1);
  149. gb0tmp=zeros(3,nshot);
  150. mtmp=zeros(3,2,nshot);
  151. for i=1:ste_info.nc
  152. gb0tmp(:,idx_gre==i)=ste_info.md{i}.gb0;
  153. db0tmp(idx_gre==i)=ste_info.md{i}.db0;
  154. mtmp(:,:,idx_gre==i)=ste_info.md{i}.m;
  155. end
  156. mot_par_test=zeros(6,length(sorted_ste.idx_shot_cenpf));
  157. A1=[x_ste(:),y_ste(:),z_ste(:)]*1e-3;
  158. for i=1:ste_info.nc
  159. sc=total(idx==i);
  160. b0_fit(:,:,:,idx==i)=...
  161. b0(:,:,:,i)+...
  162. reshape(db0tmp(sorted_ste.idx_shot_cenpf(idx==i)),...
  163. [1,1,1,sc])+...
  164. reshape(A1*...
  165. gb0tmp(:,sorted_ste.idx_shot_cenpf(idx==i)),...
  166. [nx_ste,ny_ste,nz_ste,sc]);
  167. mot_par_test(:,idx==i)=...
  168. reshape(mtmp(:,:,sorted_ste.idx_shot_cenpf(idx==i)),...
  169. [6,sc]);
  170. end
  171. if field_true(par,'use_pri_b0')
  172. para.b0=interp3_nmat(coord_main,coord_ste,read_data(rp('b0_pri.svd')));
  173. else
  174. para.b0=b0;
  175. end
  177. para.j=5;
  178. para.nm=ste_info.nc;
  179. para.n_iter_reset=par.n_iter_reset;
  180. para.n_iter=par.n_iter;
  181. for i=1:ste_info.nc
  182. md(i)=ste_info.md{i};
  183. end
  184. para=setfield(para,'md',md);
  185. para.kos=par.kos;
  186. para.en_parfor=par.en_parfor;
  187. para.intp_ker_ste=intp_ker_ste;
  188. para.intp_ker_ext=intp_ker_ext;
  189. end

ste_final_prep_nintp.m at commit 7be112e, no license · at the source

Overview

Authors: Jiaen Liu1,2, Peter van Gelderen3, Jacco A de Zwart3, Jeff H Duyn3, Yujia Huang1, Shuxian Qu4, Andrea Grant4, Edward J Auerbach4, Matt Waks4, Russell L Lagore4, Lance Delabarre4, Alireza Sadeghi‐Tarakameh4, Yigitcan Eryaman4, Gregor Adriany4, Kamil Uğurbil4, Xiaoping Wu4
ORCID iDs: Jiaen Liu, Peter van Gelderen, Jacco A de Zwart, Yujia Huang, Edward J Auerbach, Matt Waks, Russell L Lagore, Alireza Sadeghi‐Tarakameh, Gregor Adriany, Xiaoping Wu
  1. Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
  2. Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
  3. Advanced MRI Section, NINDS, NIH, Bethesda, Maryland, USA
  4. Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA
Institutions: The University of Texas Southwestern Medical Center (United States); National Institutes of Health (United States); National Institute of Neurological Disorders and Stroke (United States); University of Minnesota (United States)
Journal: Magnetic resonance in medicine, volume 96, issue 2, pages 817-825
Dates: received 2 December 2025; accepted 17 March 2026; published online 7 April 2026; in print August 2026
Type: Other · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70366 · PMID 41944307 · PMCID PMC13269191 · OpenAlex W7151605626
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics, fMRI & imaging
Keywords: 10.5 T, magnetic susceptibility χ, mesoscale whole‐brain MRI, R 2* relaxation rate, T 2*‐weighted MRI, ultrahigh field MRI
MeSH: Brain*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Adult, Algorithms, Brain Mapping, Female, Humans, Male (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIH HHS (P41 EB027061, U01 EB025144, R01 NS136490, S10 RR029672); NIBIB NIH HHS (R01 EB038654, P41 EB027061, U01 EB025144); NINDS NIH HHS (ZIANS002990, R01 NS136490); Hamon Charitable Foundation; NCRR NIH HHS (S10 RR029672); National Institutes of Health (U01 EB025144, P41 EB027061, R01 NS136490, S10 RR029672); Texas Instruments Foundation; National Institute of Neurological Disorders and Stroke (ZIANS002990)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Purpose: To demonstrate mesoscale whole‐brain T 2*‐weighted (T 2*w) MRI at 10.5 T, quantify R 2* relaxation rate and magnetic susceptibility (χ), and evaluate T 2*w contrast at such high field strength.

Methods: Multi‐echo GRE (ME‐GRE) data were collected in healthy adults at 0.5 mm isotropic resolution at 10.5 T. Whole‐brain images were reconstructed with navigator‐guided joint motion and field correction and were used for quantitative R 2* and χ mapping. Regional R 2* and χ values and R 2* contrast were analyzed in volumetric regions of interest (ROIs) and intra‐cortical surface‐based ROIs. For comparison, ME‐GRE data from the same subjects were acquired using a similar protocol at 7 T.

Results: High‐quality whole‐brain T 2*w images were obtained, enabling R 2* and χ mapping with delineation of fine‐scale brain structures. Regional R 2* analysis revealed a linear relationship between 10.5 T and 7 T R 2* values with a slope of 1.52, in agreement with previously reported linear field dependency of R 2*. Estimated χ values were field‐independent in most brain regions under consideration except for the basal ganglia where χ was observed to be lower at 10.5 T than at 7 T. The normalized R 2* contrast that is, the R 2* difference normalized by the mean R 2*, increased by about 3% between brain regions and 12% between cortical depths from 7 to 10.5 T.

Conclusion: It is feasible to achieve high‐quality mesoscale whole‐brain T 2*w MRI at 10.5 T and associated quantitative R 2* and χ mapping. Our results may aid future optimization of anatomic T 2*w brain MRI at ultrahigh field beyond 7 T.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

jiaen-liu/moco

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 7be112e313a19f8c9a8f92abcaff07414f57ee49, 29 June 2026
Languages: MATLAB (275)
Size: 317 files, 275 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (11 files), Statistics and Machine Learning Toolbox (4 files), Tools for NIfTI and ANALYZE image (MATLAB) (4 files), FSL (2 files), AFNI (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
276 files
At the source: github.com/jiaen-liu/moco

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;
  • 275 scripts, each with its path and the digest of its content;
  • 2 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

Datasets cited

Data Availability Statement

The human brain data including ME‐GRE images at both 7 and 10.5 T alongside T 1w MP2RAGE at 7 T are publicly available at https://openneuro.org/datasets/ds007418/. The MATLAB code for image reconstruction is made publicly available at https://github.com/jiaen‐liu/moco (https://github.com/jiaen-liu/moco).

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 6 keywords, 9 MeSH terms, 8 funders, 64 references.

Cite

This paper

Liu, J., van Gelderen, P., de Zwart, J. A., Duyn, J. H., Huang, Y., Qu, S., Grant, A., Auerbach, E. J., Waks, M., Lagore, R. L., Delabarre, L., Sadeghi‐Tarakameh, A., Eryaman, Y., Adriany, G., Uğurbil, K., & Wu, X. (2026). Mesoscale Whole-Brain T<sub>2</sub>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T. Magnetic resonance in medicine, 96(2), 817-825. https://doi.org/10.1002/mrm.70366

BibTeX

@article{liu2026mesoscale,
author = {Liu, Jiaen and van Gelderen, Peter and de Zwart, Jacco A and Duyn, Jeff H and Huang, Yujia and Qu, Shuxian and Grant, Andrea and Auerbach, Edward J and Waks, Matt and Lagore, Russell L and Delabarre, Lance and Sadeghi‐Tarakameh, Alireza and Eryaman, Yigitcan and Adriany, Gregor and Uğurbil, Kamil and Wu, Xiaoping},
title = {{Mesoscale Whole-Brain T\<sub\>2\</sub\>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = apr,
volume = {96},
number = {2},
pages = {817--825},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70366},
url = {https://doi.org/10.1002/mrm.70366},
pmid = {41944307},
pmcid = {PMC13269191}
}

RIS

TY - JOUR
AU - Liu, Jiaen
AU - van Gelderen, Peter
AU - de Zwart, Jacco A
AU - Duyn, Jeff H
AU - Huang, Yujia
AU - Qu, Shuxian
AU - Grant, Andrea
AU - Auerbach, Edward J
AU - Waks, Matt
AU - Lagore, Russell L
AU - Delabarre, Lance
AU - Sadeghi‐Tarakameh, Alireza
AU - Eryaman, Yigitcan
AU - Adriany, Gregor
AU - Uğurbil, Kamil
AU - Wu, Xiaoping
TI - Mesoscale Whole-Brain T<sub>2</sub>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/04/07
VL - 96
IS - 2
SP - 817
EP - 825
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70366
UR - https://doi.org/10.1002/mrm.70366
LA - en
ER -

CSL-JSON

{
"id": "10.1002/mrm.70366",
"type": "article-journal",
"title": "Mesoscale Whole-Brain T<sub>2</sub>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T",
"container-title": "Magnetic resonance in medicine",
"author": [
{
"family": "Liu",
"given": "Jiaen"
},
{
"family": "van Gelderen",
"given": "Peter"
},
{
"family": "de Zwart",
"given": "Jacco A"
},
{
"family": "Duyn",
"given": "Jeff H"
},
{
"family": "Huang",
"given": "Yujia"
},
{
"family": "Qu",
"given": "Shuxian"
},
{
"family": "Grant",
"given": "Andrea"
},
{
"family": "Auerbach",
"given": "Edward J"
},
{
"family": "Waks",
"given": "Matt"
},
{
"family": "Lagore",
"given": "Russell L"
},
{
"family": "Delabarre",
"given": "Lance"
},
{
"family": "Sadeghi‐Tarakameh",
"given": "Alireza"
},
{
"family": "Eryaman",
"given": "Yigitcan"
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{
"family": "Adriany",
"given": "Gregor"
},
{
"family": "Uğurbil",
"given": "Kamil"
},
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"family": "Wu",
"given": "Xiaoping"
}
],
"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "2",
"page": "817-825",
"DOI": "10.1002/mrm.70366",
"PMID": "41944307",
"PMCID": "PMC13269191",
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"URL": "https://doi.org/10.1002/mrm.70366",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
7
]
]
}
}

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