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Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R<sub>2</sub>* and Magnetic Susceptibility Using Tailored k-space Sampling.

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  1. [1] § Methods › MRI Data Acquisition and Analyses › Computation and Analysis of the qMRI Maps › Relaxometry and QSM ↔ MPM_QSM.m, lines 185–287 · score 0.84 · STAR QSM, dipole inversion, background field, SEPIA, ROMEO, Removal
  2. [2] § Methods › MRI Data Acquisition and Analyses › Computation and Analysis of the qMRI Maps › Relaxometry and QSM ↔ MPM_QSM_caller.m, lines 58–73 · score 0.81 · ROMEO unwrapping, SEPIA toolbox, background field, QSM, Removal, algorithm

Paper

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The authors' code

MATLAB · 289 lines · 11 KB · GPL-3.0 · 1 match

  1. %%% Description: MPM QSM pipeline
  2. % main steps:
  3. % 1) phase unwrapping and B0 map calculation using ROMEO
  4. % 2) masking based on ROMEO quality map
  5. % 3) rotation to scanner space for oblique acquisitions using SPM
  6. % 4) PDF background field removal within SEPIA toolbox
  7. % 5) star QSM for dipole inversion as default (optional: non-linear dipole inversion) within SEPIA toolbox
  8. % 6) rotation back of QSM results to image space (for comparisons with PD, R2*, R1 and MT maps) using SPM (optional: non-linear dipole inversion)
  11. %%% Publications:
  12. % Please remember to give credit to the authors of the methods used:
  13. % 1. SEPIA toolbox:
  14. % Chan, K.-S., Marques, J.P., 2021. Neuroimage 227, 117611.
  15. % 2. SPM12 - rigid body registration:
  16. % Friston KJ, et al. Magnetic Resonance in Medicine 35 (1995):346-355
  17. % 3. complex fit of the phase:
  18. % Liu, Tian, et al. MRM 69.2 (2013): 467-476.
  19. % 4. ROMEO phase uwnrapping:
  20. % Dymerska, Barbara, and Eckstein, Korbinian et al. Magnetic Resonance in Medicine (2020).
  21. % 5. PDF background field removal:
  22. % Liu, Tian, et al. NMR in Biomed. 24.9 (2011): 1129-1136.
  23. % 6. starQSM:
  24. % Wei, Hongjiang, et al. NMR in Biomed. 28.10 (2015): 1294-1303.
  26. %%% Inputs:
  27. % romeo_command : path to romeo phase uwnrapping followed by romeo command, i.e. (in linux) '/your_path/bin/romeo' or (in windows) 'D:\your_path\bin\romeo'
  28. % in_root_dir : root directory to input nifti files
  29. % out_root_dir : root directory to output nifti files
  30. % B0 : magnetic field strength, in Tesla
  31. % dipole_inv : dipole inversion method, either 'Star-QSM' or 'ndi'
  32. % 'ndi' - non-linear dipole inversion
  33. % (also known as iterative Tikhonov),
  34. % may give more contrast than Star-QSM but is less robust to noise
  35. % 'Star-QSM' - is very robust to noise and quick
  37. %%%% Inputs - directories, parameters and files specific to given contrast
  38. % ATTENTION: ensure only niftis you want to use are in that folder, with increasing echo numbering:
  39. % mag_dir : % folder with magnitude niftis
  40. % ph_dir : % folder with phase inftis
  41. % TEs : % echo time in ms
  42. % output_dir : % output QSM directory for a specific MPM contrast
  43. % calc_mean_qsm : % 'yes' or 'no' , if 'yes' it calculates mean QSM from all contrasts
  45. %%% Outputs:
  46. %%%% combined final results in out_root_dir:
  47. % QSM_all_mean.nii : mean QSM over all contrasts in scanner space (3rd dimension is along B0-axis)
  48. % QSM_all_invrot_mean.nii : mean QSM over all contrasts in image space (as acquired, for comparison with MPM quantitative maps)
  49. % QSM_pdw_t1w_mean.nii : mean QSM over PDw and T1w contrasts (without noisy MTw) in scanner space
  50. % QSM_pdw_t1w_invrot_mean.nii : mean QSM over PDw and T1w contrasts in image space
  52. %%%% final results - per contrast in subfolders in out_root_dir:
  53. % sepia_QSM.nii OR sepia_Chimap.nii : QSM in scanner space (name depends on SEPIA toolbox version)
  54. % sepia_QSM_invrot.nii : QSM in image space
  56. %%%% additional outputs:
  57. % ph.nii : two volumes (odd and even) of fitted phase
  58. % ph_romeo.nii : ph.nii unwrapped with ROMEO
  59. % quality.nii : quality map calculated by ROMEO algorithm and used for masking
  60. % mask.nii : binary mask in image space
  61. % mask_rot.nii : binary mask in scanner space
  62. % B0.nii : field map in Hz in image space
  63. % B0_rot.nii : field map in Hz in scanner space
  64. % sepia_local-field.nii.gz OR sepia_localfield.nii.gz : map of local field variations (after background field removal using PDF)
  65. % settings_romeo.txt : settings used for ROMEO unwrapping (useful if unwrapping again outside the MPM_QSM pipeline)
  66. % header_sepia.mat : header used for SEPIA toolbox (useful when exploring SEPIA GUI)
  68. % script created by Barbara Dymerska
  69. % @ UCL FIL Physics
  71. function [QSM_V, QSM , QSMinvrot_V, QSMinvrot] = MPM_QSM(para)
  73. tstart = tic ;
  75. mag_fulldir = fullfile(para.in_root_dir, para.mag_dir) ;
  76. ph_fulldir = fullfile(para.in_root_dir, para.ph_dir) ;
  78. output_fulldir = fullfile(para.out_root_dir, para.output_dir) ;
  80. if ~exist(output_fulldir, 'dir')
  81. mkdir(output_fulldir)
  82. end
  83. cd(output_fulldir)
  85. TEs = para.TEs ;
  87. if isempty(para.ph_file) && isempty(para.mag_file)
  88. ph_files = spm_select('FPList', ph_fulldir, '^.*\.(nii|img)$');
  89. mag_files = spm_select('FPList', mag_fulldir, '^.*\.(nii|img)$');
  91. ph_1tp = nifti(ph_files(1,:));
  92. ph = zeros([size(ph_1tp.dat) size(TEs,2)]);
  93. mag = zeros([size(ph_1tp.dat) size(TEs,2)]);
  94. for t = 1:size(TEs,2)
  96. ph_1tp = nifti(ph_files(t,:));
  97. ph(:,:,:,t) = ph_1tp.dat(:,:,:) ;
  99. mag_1tp = nifti(mag_files(t,:));
  100. mag(:,:,:,t) = mag_1tp.dat(:,:,:) ;
  102. end
  103. ph(~isfinite(ph))=0;
  104. mag(~isfinite(mag))=0;
  106. ph_merged_file = fullfile(output_fulldir, 'ph.nii') ;
  107. mag_merged_file = fullfile(output_fulldir, 'mag.nii') ;
  109. createNifti(ph, ph_merged_file, ph_1tp.mat)
  110. createNifti(mag, mag_merged_file, mag_1tp.mat)
  111. else
  112. ph_merged_file = fullfile(ph_fulldir, para.ph_file) ;
  113. mag_merged_file = fullfile(mag_fulldir, para.mag_file) ;
  114. end
  116. disp('phase unwrapping with ROMEO and:')
  117. disp('...removing global mean value')
  118. disp('......field map calculation')
  119. disp('.........saving quality map for masking')
  120. ph_romeo_file = fullfile(output_fulldir, 'ph_romeo.nii') ;
  122. status = system(sprintf('%s -p %s -m %s -o %s -t [%s] -q -B --weights 111111 --phase-offset-correction bipolar', para.romeo_command, ph_merged_file, mag_merged_file, ph_romeo_file, num2str(TEs))) ;
  124. if status == 1
  125. error('ROMEO did not run properly - check your installation path')
  126. end
  128. %% field map rotation
  129. % defining affine matrix in scanner space for data rotation to scanner
  130. % space with mantaining the same image origin (i.e. no translation)
  131. FM_V = spm_vol('B0.nii') ;
  132. FM = nifti('B0.nii');
  133. disp('field map rotation to scanner space')
  134. data_dim = size(FM.dat) ;
  135. Z = spm_imatrix(FM.mat) ;
  136. pixdim = Z(7:9);
  138. mat_image = FM.mat ;
  139. O = mat_image\[0 0 0 1]' ;
  140. O = O(1:3)' ;
  142. mat_scanner(1,:) = [0 0 pixdim(3) -pixdim(3)*O(3)] ;
  143. mat_scanner(2,:) = [pixdim(1) 0 0 -pixdim(1)*O(1)] ;
  144. mat_scanner(3,:) = [0 pixdim(2) 0 -pixdim(2)*O(2)] ;
  145. mat_scanner(4,:) = [0 0 0 1] ;
  148. img2scanner_mat = mat_image\mat_scanner ;
  149. FMrot = zeros(data_dim) ;
  150. data_dim_xy = data_dim(1:2);
  152. for slice = 1 : data_dim(3)
  153. FMrot(:,:,slice) = spm_slice_vol(FM_V, img2scanner_mat*spm_matrix([0 0 slice]), data_dim_xy, -7) ;
  154. end
  156. FMrot(~isfinite(FMrot)) = 0 ;
  157. createNifti(FMrot, 'B0_rot.nii', mat_scanner)
  159. %% creating mask for QSM calculation
  161. disp('quality masking')
  162. qmask = nifti('quality.nii') ;
  163. qmask = qmask.dat(:,:,:) ;
  164. qmask(~isfinite(qmask)) = 0;
  165. qmask(qmask>para.mask_thr) = 1 ;
  166. qmask(qmask<=para.mask_thr) = 0 ;
  167. % filling holes in the mask
  168. qmask = imfill(qmask,6,'holes') ;
  169. qmask = smooth3(qmask, 'gaussian') ;
  170. qmask(qmask>0.6) = 1 ;
  171. qmask(qmask<=0.6) = 0 ;
  173. qmask = int16(qmask) ;
  174. createNifti(qmask, 'mask.nii', mat_image)
  175. clear qmask
  177. mask_V = spm_vol('mask.nii') ;
  178. qmask_rot = zeros(data_dim) ;
  179. for slice = 1 : data_dim(3)
  180. qmask_rot(:,:,slice) = spm_slice_vol(mask_V, img2scanner_mat*spm_matrix([0 0 slice]), data_dim_xy, -7) ;
  181. end
  182. qmask_rot(~isfinite(qmask_rot)) = 0 ;
  183. createNifti(int16(qmask_rot), 'mask_rot.nii', mat_scanner)
  185. %% SEPIA - background field removal and dipole inversion yielding final QSM
  187. disp('creating SEPIA header')
  188. B0 = para.B0 ;
  189. CF = B0*42.58*1e6; % imaging frequency, in Hz (B0*gyromagnetic_ratio*1e6)
  190. delta_TE = 1; % echo spacing, in second - we have already combined data, in such situation set to 1
  191. TE = 1 ;
  192. B0_dir = para.B0_dir; % main magnetic field direction, it's always [0,1,0] because the images are resliced so that 2nd dimension is aligned with B0
  193. matrixSize = data_dim ; % image matrix size
  194. voxelSize = pixdim ; % spatial resolution of the data, in mm
  195. header_fullfile = fullfile(output_fulldir, 'header_sepia.mat') ;
  196. save(header_fullfile, 'B0', 'B0_dir', 'CF', 'TE', 'delta_TE', 'matrixSize', 'voxelSize')
  198. % general SEPIA parameters
  199. sepia_addpath
  200. algorParam.general.isBET = 0 ;
  201. algorParam.general.isInvert = 1 ;
  202. algorParam.general.isGPU = 0 ;
  203. output_basename = fullfile(output_fulldir, 'sepia') ;
  205. % inputs for background field removal
  206. input(1).name = 'B0_rot.nii' ;
  207. input(2).name = 'mask_rot.nii' ;
  208. input(4).name = header_fullfile ;
  210. algorParam.bfr.refine = 0 ;
  211. algorParam.bfr.erode_radius = 1 ;
  212. algorParam.bfr.method = 'pdf' ;
  213. algorParam.bfr.tol = 0.1 ;
  214. algorParam.bfr.iteration = 50 ;
  215. algorParam.bfr.padSize = 30 ;
  218. % inputs for dipole inversion
  219. algorParam.qsm.method = para.dipole_inv ;
  220. if strcmp(algorParam.qsm.method , 'ndi')
  222. algorParam.qsm.method = 'ndi' ;
  223. algorParam.qsm.tol = 1 ;
  224. algorParam.qsm.maxiter = 200 ;
  225. algorParam.qsm.stepSize = 1 ;
  227. elseif strcmp(algorParam.qsm.method , 'Star-QSM')
  229. algorParam.qsm.padsize = ones(1,3)*6 ;
  231. end
  234. disp('background field removal using PDF')
  235. BackgroundRemovalMacroIOWrapper(input,output_basename,input(2).name,algorParam);
  237. % added for back-compatibility to older SEPIA versions
  238. if exist('sepia_local-field.nii.gz','file') == 2
  239. input(1).name = 'sepia_local-field.nii.gz' ;
  240. elseif exist('sepia_localfield.nii.gz','file') == 2
  241. input(1).name = 'sepia_localfield.nii.gz' ;
  242. else
  243. error('no local field file found in output dir')
  244. end
  246. fprintf('dipole inversion using %s', algorParam.qsm.method)
  247. QSMMacroIOWrapper(input,output_basename,input(2).name,algorParam);
  250. disp('rotation of QSM back to the original image space')
  251. % added for back-compatibility to older SEPIA versions
  252. if exist('sepia_QSM.nii.gz') == 2
  253. gunzip('sepia_QSM.nii.gz')
  254. QSM = nifti('sepia_QSM.nii') ;
  255. QSM_V = spm_vol('sepia_QSM.nii');
  256. elseif exist('sepia_Chimap.nii.gz') == 2
  257. gunzip('sepia_Chimap.nii.gz')
  258. QSM = nifti('sepia_Chimap.nii') ;
  259. QSM_V = spm_vol('sepia_Chimap.nii');
  260. else
  261. error('no QSM maps in output dir')
  262. end
  264. QSM = QSM.dat(:,:,:) ;
  266. scanner2img_mat = mat_scanner\mat_image ;
  267. QSMinvrot = zeros(data_dim) ;
  269. for slice = 1 : data_dim(3)
  270. QSMinvrot(:,:,slice) = spm_slice_vol(QSM_V, scanner2img_mat*spm_matrix([0 0 slice]), data_dim_xy, -7) ;
  271. end
  273. QSMinvrot_V = FM_V ;
  274. QSMinvrot_V.fname = 'sepia_QSM_invrot.nii';
  275. spm_write_vol(QSMinvrot_V, QSMinvrot);
  277. warning('off');
  278. delete sepia_mask-qsm.nii.gz sepia_QSM.nii.gz sepia_mask_QSM.nii.gz sepia_Chimap.nii.gz
  279. if strcmp(para.data_cleanup,'small') || strcmp(para.data_cleanup,'big')
  280. delete mag.nii corrected_phase.nii mask.nii mask_rot.nii ph.nii ph_romeo.nii quality.nii sepia_local-field.nii.gz sepia_localfield.nii.gz
  281. end
  282. if strcmp(para.data_cleanup,'big')
  283. delete B0.nii B0_rot.nii
  284. end
  285. warning('on');
  286. sprintf('finished after %s' , secs2hms(toc(tstart)))
  287. end

MPM_QSM.m at commit f05a44f, under GPL-3.0 · at the source

Overview

Authors: Quentin Raynaud1, Thomas Dardano1, Rita Oliveira1, Giulia Di Domenicantonio1, Tobias Kober2,3,4, Christopher W. Roy2, Ruud B. van Heeswijk2, Antoine Lutti1
ORCID iDs: Rita Oliveira, Tobias Kober, Antoine Lutti
  1. Laboratory for Research in Neuroimaging, Department for Clinical Neuroscience Lausanne University Hospital and University of Lausanne Lausanne Switzerland
  2. Department of Diagnostic and Interventional Radiology Lausanne University Hospital and University of Lausanne Lausanne Switzerland
  3. Advanced Clinical Imaging Technology Siemens Healthineers International AG Lausanne Switzerland
  4. LTS5, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); École Polytechnique Fédérale de Lausanne (Switzerland)
Journal: NMR in biomedicine, volume 39, issue 6, article e70305
Dates: received 3 November 2025; accepted 20 April 2026; published online 10 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/nbm.70305 · PMID 42108686 · PMCID PMC13158452 · OpenAlex W7160874917
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics, fMRI & imaging
Keywords: brain, cardiac‐induced noise, MRI relaxometry, physiological noise, QSM, quantitative MRI, R 2*
MeSH: Artifacts*, Brain*, Brain Mapping*, Heart*, Magnetic Resonance Imaging*, Pulsatile Flow*, Signal-To-Noise Ratio*, Adult, Female, Humans, Male, Reproducibility of Results (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (320030_184784, CR00I5‐235940, 32003B_182615, CRSII5_202276); Fondation Roger de Spoelberch
Citations: not cited yet (Europe PMC); 72 references in the paper

Abstract

Maps of the transverse relaxation rate R 2* and magnetic susceptibility (χ) are computed from gradient‐echo data and are sensitive to signal instabilities induced by cardiac pulsation. Here, we introduce two k‐space sampling strategies that aim to mitigate the impact of cardiac‐induced noise in brain maps of R 2* and χ.

The proposed strategies are based on the higher level of cardiac‐induced noise near the k‐space centre compared to the periphery. Using CArtesian trajectory with Spiral PRofile (CASPR), the first strategy allows for the acquisition of a specific number of averages at each k‐space location, derived from the local level of cardiac‐induced noise. The second strategy uses cardiac triggering to synchronize the acquisition near the k‐space centre with the cardiac cycle in real time. We compared the variability across four repetitions of R 2* and χ maps computed from data acquired using both strategies and with a standard linear trajectory.

Data were acquired in 10 healthy volunteers. Compared to linear trajectory, CASPR reduced the variability of R 2* and χ maps across repetitions by 22% and 16% across the whole brain, reaching over 30% in inferior brain regions, for a 14% increase in scan time. CASPR also reduced the level of aliasing artefacts from pulsating blood vessels. Cardiac triggering did not reduce the variability of R 2* or χ maps.

CASPR can be designed to mitigate cardiac‐induced noise in brain maps of the MRI parameters R 2* and χ. Synchronization of data acquisition with the cardiac cycle did not reduce the level of cardiac‐induced noise.

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 2 matches between paragraphs and lines of code.

mchiew/grappa-tools

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9d7cbbc999641ac8b5d7fa30b6eee1e92007f7b5, 17 July 2026
Languages: MATLAB (12), Python (1)
Size: 17 files, 13 scripts
Software Heritage: not checked
Found in: the text, “Image Reconstruction”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/mchiew/grappa-tools

fil-physics/MPM_QSM

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f05a44f8d8d6e489084fbcfeb9dbf79b0955d174, 20 March 2026
Languages: MATLAB (246), C/C++ (52), C++ (39), C (5)
Size: 462 files, 342 scripts
Software Heritage: not checked
Found in: the text, “Relaxometry and QSM”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (11 files), Image Processing Toolbox (9 files), Signal Processing Toolbox (2 files), SPM (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
344 files
At the source: github.com/fil-physics/MPM_QSM

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;
  • 355 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

One of the 5D datasets used for the optimization of the sampling strategies can be found here (DOI:10.5281/zenodo.7428605 (https://doi.org/10.5281/zenodo.7428605)). The high‐resolution dataset is available online here (DOI:10.5281/zenodo.12685105 (https://doi.org/10.5281/zenodo.12685105)).

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 keywords, 12 MeSH terms, 2 funders, 71 references.

Cite

This paper

Raynaud, Q., Dardano, T., Oliveira, R., Di Domenicantonio, G., Kober, T., Roy, C. W., van Heeswijk, R. B., & Lutti, A. (2026). Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R&lt;sub&gt;2&lt;/sub&gt;* and Magnetic Susceptibility Using Tailored k-space Sampling. NMR in biomedicine, 39(6), e70305. https://doi.org/10.1002/nbm.70305

BibTeX

@article{raynaud2026reducing,
author = {Raynaud, Quentin and Dardano, Thomas and Oliveira, Rita and Di Domenicantonio, Giulia and Kober, Tobias and Roy, Christopher W. and van Heeswijk, Ruud B. and Lutti, Antoine},
title = {{Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R\&lt;sub\&gt;2\&lt;/sub\&gt;* and Magnetic Susceptibility Using Tailored k-space Sampling}},
journal = {NMR in biomedicine},
year = {2026},
month = jun,
volume = {39},
number = {6},
pages = {e70305},
publisher = {Wiley},
issn = {0952-3480},
doi = {10.1002/nbm.70305},
url = {https://doi.org/10.1002/nbm.70305},
pmid = {42108686},
pmcid = {PMC13158452}
}

RIS

TY - JOUR
AU - Raynaud, Quentin
AU - Dardano, Thomas
AU - Oliveira, Rita
AU - Di Domenicantonio, Giulia
AU - Kober, Tobias
AU - Roy, Christopher W.
AU - van Heeswijk, Ruud B.
AU - Lutti, Antoine
TI - Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R&lt;sub&gt;2&lt;/sub&gt;* and Magnetic Susceptibility Using Tailored k-space Sampling
T2 - NMR in biomedicine
J2 - NMR Biomed
PY - 2026
DA - 2026/06/01
VL - 39
IS - 6
SP - e70305
SN - 0952-3480
PB - Wiley
DO - 10.1002/nbm.70305
UR - https://doi.org/10.1002/nbm.70305
LA - en
ER -

CSL-JSON

{
"id": "10.1002/nbm.70305",
"type": "article-journal",
"title": "Reducing Noise Induced by Cardiac Pulsatility in Brain Maps of R&lt;sub&gt;2&lt;/sub&gt;* and Magnetic Susceptibility Using Tailored k-space Sampling",
"container-title": "NMR in biomedicine",
"author": [
{
"family": "Raynaud",
"given": "Quentin"
},
{
"family": "Dardano",
"given": "Thomas"
},
{
"family": "Oliveira",
"given": "Rita"
},
{
"family": "Di Domenicantonio",
"given": "Giulia"
},
{
"family": "Kober",
"given": "Tobias"
},
{
"family": "Roy",
"given": "Christopher W."
},
{
"family": "van Heeswijk",
"given": "Ruud B."
},
{
"family": "Lutti",
"given": "Antoine"
}
],
"container-title-short": "NMR Biomed",
"volume": "39",
"issue": "6",
"page": "e70305",
"DOI": "10.1002/nbm.70305",
"PMID": "42108686",
"PMCID": "PMC13158452",
"ISSN": "0952-3480",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/nbm.70305",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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