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The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification.

Code ↔ Paper

4 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.

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  1. [1] § Methods › Experiment 1: In Vivo Test–Retest Dataset › MRS Data Processing ↔ run_sLASER_makebasisset.m, lines 1–117 · score 0.94 · spectral width, sLASER, Asc, GPC, GSH, Glc
  2. [2] § Methods › Experiment 1: In Vivo Test–Retest Dataset › MR Scanning Protocol ↔ run_sLASER_makebasisset.m, lines 1–117 · score 0.75 · spectral width, sLASER, flip angle, acceleration, parallel, phased
  3. [3] § Methods › Experiment 1: In Vivo Test–Retest Dataset › MR Scanning Protocol ↔ dependencies/sim_myslaser.m, lines 27–120 · score 0.59 · sLASER, flip angle, coil, matrix, phased, Water
  4. [4] § Methods › Experiment 1: In Vivo Test–Retest Dataset › MRS Data Processing ↔ dependencies/generateSysTemp.m, the whole file · a weak match · score 0.56 · Ala, Asp, Gln, Lac, NAA, GABA

Paper

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The authors' code

MATLAB · 357 lines · 13 KB · CC0-1.0 · 2 matches

  1. % run_sLASER_makebasisset
  2. %
  3. % Contributors:
  4. %
  5. % Jamie Near, McGill University, 2015
  6. % Georg Oeltzschner, Johns Hopkins University School of Medicine, 2019
  7. % Muhammad G Saleh, Johns Hopkins University School of Medicine, 2019
  8. % Dana Goerzen and Jamie Near, McGill University, 2021
  9. % Niklaus Zölch, Universität Zürich, 2024
  10. % Jessica Archibald, Weill Cornell Medicine, 2024
  11. % Mark Mikkelsen, Weill Cornell Medicine, 2026
  12. %
  13. % DESCRIPTION & MODIFICATIONS:
  14. %
  15. % This script was modified by Niklaus Zölch & Jessica Archibald to include:
  16. %
  17. % a. A 0 ppm reference peak, useful depending on the fitting software
  18. % b. A loop to run through the selected metabolites
  19. % c. Saving .raw, .png, and .mat files
  20. % d. Output a .pdf and .basis file using modified functions from Osprey
  21. %
  22. % USAGE:
  23. %
  24. % This script simulates a semi-LASER experiment with fully shaped
  25. % refocusing pulses. Coherence order filtering is employed to only simulate
  26. % desired signals. This results in a 4x speed-up compared to phase cycling
  27. % (see deprecated run_simSemiLASERShaped_fast_phCyc.m). Furthermore,
  28. % simulations are run at various locations in space to account for the
  29. % within-voxel spatial variation of the metabolite signal. Summation across
  30. % spatial positions is performed. The MATLAB parallel computing toolbox
  31. % (parfor loop) was used to accelerate the simulations. Acceleration is
  32. % currently performed in the direction of the slice selective pulse along
  33. % the x-direction, but this can be changed. Up to a factor of 12
  34. % acceleration can be achieved using this approach. To achieve faster
  35. % perfomance compared to the original 'run_simSemiLASER_shaped.m' function,
  36. % this code uses the method described by Zhang et al. (2017)
  37. % doi:10.1002/mp.12375. Some additional acceleration is currently performed
  38. % using parfor loops in both x and y directions. To enable the use of the
  39. % MATLAB parallel computing toolbox, initialize the multiple worker nodes
  40. % using "matlabpool size X" where "X" is the number of available processing
  41. % nodes. If the parallel processing toolbox is not available, then replace
  42. % the "parfor" loop with a "for" loop.
  44. % INPUTS:
  45. %
  46. % To run this script, there is technically only one input argument:
  47. % spinSys = spin system to simulate
  48. % However, the user should also edit the following parameters as
  49. % desired before running the function:
  50. % refocWaveform = name of refocusing pulse waveform.
  51. % refTp = duration of refocusing pulses[ms]
  52. % Bfield = Magnetic field strength in [T]
  53. % Npts = number of spectral points
  54. % sw = spectral width [Hz]
  55. % Bfield = magnetic field strength [Tesla]
  56. % lw = linewidth of the output spectrum [Hz]
  57. % thkX = slice thickness of x refocusing pulse [cm]
  58. % thkY = slice thickness of y refocusing pulse [cm]
  59. % fovX = full simulation FOV in the x direction [cm]
  60. % fovY = full simulation FOV in the y direction [cm]
  61. % nX = number of spatial grid points to simulate in x-direction
  62. % nY = number of spatial grid points to simulate in y-direction
  63. % taus = vector of pulse sequence timings [ms]
  64. %
  65. % OUTPUTS:
  66. %
  67. % out = Simulation results, summed over all space.
  69. clear;
  70. clc;
  71. close all;
  75. % ************ INPUT PARAMETERS **********************************
  77. % Define the variable Basis_name at the beginning of your script
  78. basis_name='lcm_gamma_new.basis'; %keep "_gamma_"
  79. main_dir=fileparts(mfilename("fullpath"));
  80. addpath(genpath(main_dir));
  81. ToolboxCheck;
  82. output_folder=fullfile(main_dir,'my_basis'); % or select a folder somewhere else e.g. '~/Desktop/makebasisset_output'
  83. save_result=true;
  84. complete_run=true; % true -> overwrite mode: runs sim_spinsys for all metabolites to build full basis set: % false -> append mode: runs sim_spinsys for selected metabolites and adds to existing set
  85. show_plots=false;
  86. vendor='Philips';
  87. sequence='sLASER';
  88. refocWaveform='standardized_goia.txt'; %name of refocusing pulse waveform
  89. flip_angle=180;
  90. refTp=4.4496; %duration of refocusing pulses[ms]
  91. Npts=4096; %number of spectral points
  92. sw=4000; %spectral width [Hz]
  93. lw=2; %linewidth of the output spectrum [Hz]
  94. Bfield=3; %Magnetic field strength in [T]
  95. thkX=2.4; %slice thickness of x refocusing pulse [cm]
  96. thkY=2.2; %slice thickness of y refocusing pulse [cm]
  97. fovX=3; %size of the full simulation Field of View in the x-direction [cm]
  98. fovY=3; %size of the full simulation Field of View in the y-direction [cm]
  99. nX=64; %Number of grid points to simulate in the x-direction
  100. nY=64; %Number of grid points to simulate in the y-direction
  101. x=linspace(-fovX/2,fovX/2,nX); %X positions to simulate [cm]
  102. y=linspace(-fovY/2,fovY/2,nY);
  103. te=32;%timing of the pulse sequence [ms]
  104. centreFreq=2.02; %Centre frequency of MR spectrum [ppm]
  105. B1max=[22]; %B1max for refocusing pulses; if empty, B1max is calculated automatically
  107. fovX=-x(1)+x(end);
  108. fovY=-y(1)+y(end);
  110. % spin systems
  111. spinSysList={'PE', 'Asc', 'Scyllo','Glu','Cr','NAA','NAAG','PCr','GSH','Gly','Glc','GPC',...
  112. 'PCh','Ala','Asp','GABA', 'Gln', 'Ins', 'Lac', 'Tau'};
  114. % shift
  115. shift_in_ppm=(4.65-centreFreq);
  117. % ************ END OF INPUT PARAMETERS BY USER **********************************
  119. %%JA edit: confirmation popup with current-run and final basis-set contents
  120. current_run_summary=strjoin(spinSysList,', ');
  121. if numel(current_run_summary) > 260
  122. current_run_summary=[current_run_summary(1:260) ' ...'];
  123. end
  125. existing_basis_folder=fullfile(output_folder,'matfiles_post');
  126. existing_basis_mets={};
  127. if exist(existing_basis_folder,'dir') && ~complete_run
  128. existing_basis_files=dir(fullfile(existing_basis_folder,'*.mat'));
  129. existing_basis_mets=cell(size(existing_basis_files));
  130. for existing_idx=1:numel(existing_basis_files)
  131. [~,existing_basis_mets{existing_idx},~]=fileparts(existing_basis_files(existing_idx).name);
  132. end
  133. end
  135. if complete_run
  136. final_basis_mets=spinSysList(:);
  137. else
  138. final_basis_mets=unique([existing_basis_mets(:); spinSysList(:)],'stable');
  139. end
  141. final_basis_summary=strjoin(final_basis_mets',', ');
  142. if numel(final_basis_summary) > 260
  143. final_basis_summary=[final_basis_summary(1:260) ' ...'];
  144. end
  146. confirmation_lines={
  147. 'Are you sure you want to simulate a basis set with:'
  148. ' '
  149. ['Basis file: ' basis_name]
  150. ['Output folder: ' output_folder]
  151. ['Vendor / Sequence: ' vendor ' / ' sequence]
  152. ['Refocusing waveform: ' refocWaveform]
  153. ['Flip angle: ' num2str(flip_angle) ' deg']
  154. ['Refocusing duration: ' num2str(refTp) ' ms']
  155. ['B-field: ' num2str(Bfield) ' T']
  156. ['TE: ' num2str(te) ' ms']
  157. ['Npts / SW / LW: ' num2str(Npts) ' / ' num2str(sw) ' Hz / ' num2str(lw) ' Hz']
  158. ['Slice thickness X/Y: ' num2str(thkX) ' / ' num2str(thkY) ' cm']
  159. ['FOV X/Y: ' num2str(fovX) ' / ' num2str(fovY) ' cm']
  160. ['Grid points X/Y: ' num2str(nX) ' / ' num2str(nY)]
  161. ['Centre frequency: ' num2str(centreFreq) ' ppm']
  162. ['B1max: ' mat2str(B1max)]
  163. ' '
  164. ['You are now simulating: ' current_run_summary]
  165. ['Your final basis set will contain: ' final_basis_summary]
  166. };
  168. confirmation_text=sprintf('%s\n',confirmation_lines{:});
  170. popup_handle=dialog( ...
  171. 'Name','Confirm Basis Set Simulation', ...
  172. 'Position',[200 120 760 520], ...
  173. 'Color',[0.97 0.97 0.99], ...
  174. 'WindowStyle','modal');
  175. setappdata(popup_handle,'popup_choice','No');
  177. uicontrol( ...
  178. 'Parent',popup_handle, ...
  179. 'Style','text', ...
  180. 'String','Basis Set Simulation Check', ...
  181. 'Position',[25 475 320 26], ...
  182. 'HorizontalAlignment','left', ...
  183. 'FontSize',16, ...
  184. 'FontWeight','bold', ...
  185. 'BackgroundColor',[0.97 0.97 0.99], ...
  186. 'ForegroundColor',[0.12 0.18 0.32]);
  188. uicontrol( ...
  189. 'Parent',popup_handle, ...
  190. 'Style','edit', ...
  191. 'Max',2, ...
  192. 'Min',0, ...
  193. 'Enable','inactive', ...
  194. 'String',confirmation_text, ...
  195. 'Position',[25 85 710 380], ...
  196. 'HorizontalAlignment','left', ...
  197. 'FontSize',12, ...
  198. 'BackgroundColor',[1 1 1]);
  200. uicontrol( ...
  201. 'Parent',popup_handle, ...
  202. 'Style','pushbutton', ...
  203. 'String','No', ...
  204. 'Position',[520 22 90 38], ...
  205. 'FontSize',12, ...
  206. 'Callback',@(src,evt) local_set_popup_choice(popup_handle,'No'));
  208. uicontrol( ...
  209. 'Parent',popup_handle, ...
  210. 'Style','pushbutton', ...
  211. 'String','Yes', ...
  212. 'Position',[625 22 90 38], ...
  213. 'FontSize',12, ...
  214. 'FontWeight','bold', ...
  215. 'BackgroundColor',[0.23 0.56 0.34], ...
  216. 'ForegroundColor',[1 1 1], ...
  217. 'Callback',@(src,evt) local_set_popup_choice(popup_handle,'Yes'));
  219. set(popup_handle,'CloseRequestFcn',@(src,evt) local_set_popup_choice(popup_handle,'No'));
  220. uiwait(popup_handle);
  222. popup_choice=getappdata(popup_handle,'popup_choice');
  223. if ishandle(popup_handle)
  224. delete(popup_handle);
  225. end
  227. if ~strcmp(popup_choice,'Yes')
  228. fprintf('\nSimulation cancelled by user before launch.\n\n');
  229. return;
  230. end
  233. if show_plots
  234. vis_flag='on'; %#ok<*UNRCH>
  235. else
  236. vis_flag='off';
  237. end
  240. % if all should be rerun then remove the output folder
  241. if exist(output_folder,'dir') && complete_run
  242. rmdir(output_folder,'s');
  243. end
  245. % folders for saving
  246. save_out_mat = fullfile(output_folder,'matfiles_pre');
  247. save_figure = fullfile(output_folder,'figures');
  248. save_raw = fullfile(output_folder,'raw');
  249. save_out_mat_end = fullfile(output_folder,'matfiles_post');
  251. folders = {save_out_mat, save_figure, save_raw, save_out_mat_end};
  253. % create folders if needed
  254. for k = 1:numel(folders)
  255. if ~exist(folders{k},'dir')
  256. mkdir(folders{k});
  257. end
  258. end
  259. %--------------------------------------------------------------------------
  260. %Load RF waveform
  261. %--------------------------------------------------------------------------
  262. rfPulse=io_loadRFwaveform(refocWaveform,'ref',0,B1max);
  263. %--------------------------------------------------------------------------
  264. %--------------------------------------------------------------------------
  265. sysRef.J=0;
  266. sysRef.shifts=0;
  267. sysRef.scaleFactor=1;
  268. sysRef.name='Ref_0ppm';
  269. sysRef.centreFreq=centreFreq;
  270. ref=run_mysLASERShaped_fast(rfPulse,refTp,Npts,sw,lw,Bfield,thkX,thkY,x,y,te,sysRef,flip_angle);
  272. tau1=15; %fake timing
  273. tau2=13; %fake timing
  274. refjustforppmrange=sim_press(Npts,sw,Bfield,lw,sysRef,tau1,tau2);
  275. %-------------------------------------------------------------------------
  277. %------------------------------------------------
  278. % Shift
  279. %------------------------------------------------
  280. freqShift_hz=shift_in_ppm*(Bfield*42.577478); % in Hz
  281. %-------------------------------------------------------------------------
  282. % Add shift here for the ref
  283. %-------------------------------------------------------------------------
  284. ref.fids=ref.fids.*exp(-(1i*2*pi*freqShift_hz).*ref.t).';
  285. %--------------------------------------------------------------------------
  286. % Additional Metabolites
  287. [sysETH,sysAcetate,sysAcac,sysSucc,sysGlyc,sysVal,sysAceton,sysbHBHM]=define_spin_systems;
  289. %-------------------------------------------------------------------------
  290. %Load spin systems (for the rest)
  291. load(fullfile(main_dir,'my_mets','my_spinSystem.mat'));
  292. %-------------------------------------------------------------------------
  294. for met_nr=1:size(spinSysList,2)
  296. spinSys=spinSysList{met_nr}; %spin system to simulate
  297. sys=eval(['sys' spinSys]);
  298. % Schreibe die einfach im ersten rein
  299. sys(1).centreFreq=centreFreq;
  301. %-------------------------------------------------------------------------
  302. % Simulation
  303. %-------------------------------------------------------------------------
  304. out=run_mysLASERShaped_fast(rfPulse,refTp,Npts,sw,lw,Bfield,thkX,thkY,x,y,te,sys,flip_angle);
  306. %add w1 max
  307. out.w1max=rfPulse.w1max;
  309. % Save before the shift -
  310. save([save_out_mat,filesep,spinSys],'out');
  312. %-------------------------------------------------------------------------
  313. % Add shift here for every simulated metabolite
  314. %-------------------------------------------------------------------------
  315. out.fids=out.fids.*exp(-(1i*2*pi*freqShift_hz).*ref.t).';
  317. %-------------------------------------------------------------------------
  318. % add TMS ref
  319. %-------------------------------------------------------------------------
  320. out=op_addScans(out,ref);
  322. h=figure('Visible',vis_flag);
  323. clf(h);
  324. plot(refjustforppmrange.ppm,real(ifftshift(ifft(out.fids))),'b');
  325. set(gca,'xdir','reverse');
  326. colormap;set(gcf,'color','w');
  327. xlim([-1 5]);
  328. xlabel('ppm');
  329. title(['figure with ref',spinSys]);
  330. print(h,'-dpng','-r300',[save_figure,filesep,spinSys]);
  332. out.name=spinSys;
  333. out.centreFreq=centreFreq; % This is needed for the check within fit_LCMmakeBasis.
  336. if save_result
  337. RF=io_writelcmraw(out,[save_raw, filesep, spinSys '.raw'],spinSys);
  338. end
  340. % Saving after shift
  341. save([save_out_mat_end,filesep,spinSys],'out');
  343. end
  345. fprintf('\nRunning fit_makeLCMBasis...\n\n');
  346. close(101)
  347. BASIS=fit_makeLCMBasis(save_out_mat_end, false, [output_folder, filesep, basis_name], vendor, sequence, vis_flag);
  349. rmpath(genpath(main_dir));
  350. fprintf('\nDone! Output saved in ''%s''\n\n',output_folder);
  352. function local_set_popup_choice(popup_handle,choice_value)
  353. if ishandle(popup_handle)
  354. setappdata(popup_handle,'popup_choice',choice_value);
  355. uiresume(popup_handle);
  356. end
  357. end

run_sLASER_makebasisset.m at commit 02d0a76, under CC0-1.0 · at the source

Overview

Authors: Jessica Archibald1, Kay Chioma Igwe2, Antonia Kaiser3, Karl Landheer4, Jaimie Lee5, John L K Kramer5, Aaron T Gudmundson6, Helge J Zöllner7, Georg Oeltzschner7, Candace C Fleischer8,9, Niklaus Zölch10, Jamie Near11, Mark Mikkelsen1
ORCID iDs: Jessica Archibald, Kay Chioma Igwe, Antonia Kaiser, Karl Landheer, Jaimie Lee, Aaron T Gudmundson, Helge J Zöllner, Georg Oeltzschner, Candace C Fleischer, Jamie Near, Mark Mikkelsen
  1. Department of Radiology, Weill Cornell Medicine, New York, New York, USA
  2. Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, New York, USA
  3. CIBM Center for Biomedical Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
  4. Regeneron Genetics Center, Tarrytown, New York, USA
  5. Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
  6. The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA
  7. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  8. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
  9. Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
  10. Institute of Forensic Medicine, Universität Zürich, Zürich, Switzerland
  11. Sunnybrook Research Institute and University of Toronto, Toronto, Ontario, Canada
Institutions: Weill Cornell Medicine (United States); Columbia University (United States); Centre d'Imagerie BioMedicale (Switzerland); École Polytechnique Fédérale de Lausanne (Switzerland); Regeneron (United States) (United States); University of British Columbia (Canada); Johns Hopkins University (United States); Georgia Institute of Technology (United States); Emory University (United States); The Wallace H. Coulter Department of Biomedical Engineering (United States); University of Zurich (Switzerland); University of Toronto (Canada); Sunnybrook Research Institute
Journal: Magnetic resonance in medicine, volume 96, issue 2, pages 516-529
Dates: received 23 January 2026; accepted 28 March 2026; published online 10 April 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70380 · PMID 41964041 · PMCID PMC13269192 · OpenAlex W7153310278
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), other (organism), methods / tools (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: ANTs, FSL, magnetic resonance spectroscopy, metabolite quantification, SPM, tissue segmentation
MeSH: Brain*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Adult, Algorithms, Creatine, Female, Humans, Magnetic Resonance Spectroscopy, Male, Reproducibility of Results (* major topic)
Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Institutes of Health (K99AG080084, K99EB028828, R21EB033516, R01EB035529, DP2NS127704); NIH HHS (R01EB035529, R21EB033516, K99AG080084, DP2NS127704, K99EB028828); NIBIB NIH HHS (K99 EB028828)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Purpose: Quantification of metabolite concentrations using MRS requires tissue‐dependent signal corrections. Accurate estimation of voxel tissue composition is therefore essential. Commonly used brain tissue segmentation tools differ in their algorithms and implementation, potentially introducing variability in MRS‐derived concentration estimates. This study investigates the impact and reliability of tissue segmentation on metabolite quantification.

Methods: Three segmentation tools (ANTs, FSL, SPM) were evaluated using an in vivo test–retest MRI/MRS dataset. Voxelwise GM/WM/CSF fractions were applied to compute tissue‐corrected total creatine (tCr) concentrations. Linear mixed‐effects modeling, variance‐component partitioning, and intraclass correlation coefficients (ICCs) quantified tool‐, session‐, and participant‐related variance under permutation scenarios that isolated segmentation‐ and MRS‐related effects. As a benchmark for segmentation performance, comparisons with manually segmented data were conducted across three brain regions.

Results: Segmentation tools produced systematically different tissue fractions that propagated into differences in tCr concentration estimates. Variance partitioning attributed 56.8%, 50.0%, and 51.3% of total tCr concentration variability to segmentation tool across the three permutations, with participant‐specific factors accounting for 34.7%, 36.2%, and 28.5%, respectively. When segmentation variability was held constant, test–retest reliability was high (ICC > 0.8) but dropped to ∼0.5 when both segmentation and MRS variability varied. Agreement with manual segmentation was region‐ and tool‐dependent, with the lowest agreement in the thalamus.

Conclusion: Tissue segmentation contributes substantially to the variability in MRS‐derived metabolite concentration estimates. These results underscore the need for transparent segmentation reporting and data sharing to ensure reproducibility and cross‐study comparability in MRS research.

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 4 matches between paragraphs and lines of code.

openneuro:ds006444

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: openneuro.org/datasets/ds006444

arcj-hub/BasisSetSimulation

License: CC0-1.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 02d0a768bd09fb4318ad3679860eb9af465a4ac5, 15 May 2026
Languages: MATLAB (15)
Size: 23 files, 15 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
17 files
At the source: github.com/arcj-hub/BasisSetSimulation

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 scripts, each with its path and the digest of its content;
  • 4 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

All MR data and code used in this study are publicly available on OpenNeuro at https://openneuro.org/datasets/ds006444. The basis set simulation code is available on GitHub at https://github.com/arcj‐hub/BasisSetSimulation (https://github.com/arcj-hub/BasisSetSimulation). The code for quantification and the statistical analyses is included in the OpenNeuro repository. Note that all anatomical MR images were de‐faced using PyDeface (v2.0.2, https://github.com/poldracklab/pydeface) to allow public sharing. The MRBrainS dataset is available at https://mrbrains13.isi.uu.nl/.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 6 keywords, 11 MeSH terms, 3 funders, 58 references.

Cite

This paper

Archibald, J., Igwe, K. C., Kaiser, A., Landheer, K., Lee, J., Kramer, J. L. K., Gudmundson, A. T., Zöllner, H. J., Oeltzschner, G., Fleischer, C. C., Zölch, N., Near, J., & Mikkelsen, M. (2026). The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification. Magnetic resonance in medicine, 96(2), 516-529. https://doi.org/10.1002/mrm.70380

BibTeX

@article{archibald2026impact,
author = {Archibald, Jessica and Igwe, Kay Chioma and Kaiser, Antonia and Landheer, Karl and Lee, Jaimie and Kramer, John L K and Gudmundson, Aaron T and Zöllner, Helge J and Oeltzschner, Georg and Fleischer, Candace C and Zölch, Niklaus and Near, Jamie and Mikkelsen, Mark},
title = {{The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = apr,
volume = {96},
number = {2},
pages = {516--529},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70380},
url = {https://doi.org/10.1002/mrm.70380},
pmid = {41964041},
pmcid = {PMC13269192}
}

RIS

TY - JOUR
AU - Archibald, Jessica
AU - Igwe, Kay Chioma
AU - Kaiser, Antonia
AU - Landheer, Karl
AU - Lee, Jaimie
AU - Kramer, John L K
AU - Gudmundson, Aaron T
AU - Zöllner, Helge J
AU - Oeltzschner, Georg
AU - Fleischer, Candace C
AU - Zölch, Niklaus
AU - Near, Jamie
AU - Mikkelsen, Mark
TI - The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/04/10
VL - 96
IS - 2
SP - 516
EP - 529
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70380
UR - https://doi.org/10.1002/mrm.70380
LA - en
ER -

CSL-JSON

{
"id": "10.1002/mrm.70380",
"type": "article-journal",
"title": "The Impact and Reliability of Tissue Segmentation on In Vivo Magnetic Resonance Spectroscopy Metabolite Quantification",
"container-title": "Magnetic resonance in medicine",
"author": [
{
"family": "Archibald",
"given": "Jessica"
},
{
"family": "Igwe",
"given": "Kay Chioma"
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{
"family": "Kaiser",
"given": "Antonia"
},
{
"family": "Landheer",
"given": "Karl"
},
{
"family": "Lee",
"given": "Jaimie"
},
{
"family": "Kramer",
"given": "John L K"
},
{
"family": "Gudmundson",
"given": "Aaron T"
},
{
"family": "Zöllner",
"given": "Helge J"
},
{
"family": "Oeltzschner",
"given": "Georg"
},
{
"family": "Fleischer",
"given": "Candace C"
},
{
"family": "Zölch",
"given": "Niklaus"
},
{
"family": "Near",
"given": "Jamie"
},
{
"family": "Mikkelsen",
"given": "Mark"
}
],
"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "2",
"page": "516-529",
"DOI": "10.1002/mrm.70380",
"PMID": "41964041",
"PMCID": "PMC13269192",
"ISSN": "0740-3194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/mrm.70380",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
10
]
]
}
}

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