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Single-Shot 2D Radial Echo Planar Imaging for Functional MRI.

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  1. [1] § Theory › Trajectory and KWIC Filter Design ↔ trajectory/mk2DrEPItraj.m, lines 5–16 · score 0.60 · GA rotations, Golden angle, pseudo, blipping, gradient, Trajectory
  2. [2] § Methods › Image Reconstruction ↔ nufft/nufft/example_nufft1_reverse.m, the whole file · a weak match · score 0.51 · Fourier transformation, component, iterative, NUFFT, linear, fast

Paper

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The authors' code

MATLAB · 278 lines · 9.9 KB · no license · 1 match

  1. %% generate 2D radial EPI trajectory (as used in ss-rEPI publication)
  2. clear
  3. addpath(genpath('xxx'))
  5. %% Script options
  6. write_files = 0;
  7. trajnum = 66;
  9. GreRotType = 3; % 0: arbiturar angle between grad echoes, needs to set RotEcho in deg.
  10. rot = 10; % 1: sequentially, if SeqOpt =1 will optimize gradients for whirl gradients (combine rot blipping with downwinding (flower shape)
  11. SeqOpt = 0; % 2: sGA rotation, n defines order of small Golden Angle
  12. n = 6; % 3: pseudo golden angle
  13. Nturns = 7; % target number of 180 deg turns during course of GRE train
  15. do_simulation = 1; % do multi blade k space simulation (not saved)
  16. Nrot = 7; % number of shots for simulation
  18. %% Sampling parameters
  19. fovx = 21;
  20. nx = 104; % sampling points in x (&y)
  21. Nechoes = 53; % number of GRE lines, will be adjusted in pseudo GA type
  23. %% Define constants
  24. PN = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199];
  25. gam = 26751; % rad/sec/g
  26. gambar = gam/2/pi; % Hz/g
  27. dgdtmax = 15000; % gauss/cm/sec
  28. gmax = 3; % gauss/cm
  29. dt = 10E-6;
  30. gscale = gmax;
  32. %% Initialize rotation between grandient echoes
  33. tau = (1+sqrt(5))/2;
  34. ga_all = pi./(tau+(1:10)-1);
  35. ga1 = pi/(tau+1-1);
  36. gan = pi/(tau+n-1);
  37. RotEcho = rot /360*2*pi;
  39. %% Determine Rotation between GRE lines
  40. NechoLines=Nechoes;
  41. switch GreRotType
  42. case 0
  43. ga = RotEcho;
  44. case 1
  45. ga = 1*pi/Nechoes;
  46. case 2
  47. ga = gan;
  48. case 3
  49. [~,closestIndex] = min(abs(Nechoes-PN));
  50. Ne = PN(closestIndex);
  51. ga_temp = Nturns*pi/Ne;
  52. [~,closestIndex] = min(abs(ga_temp-ga_all));
  53. ga_temp = ga_all(closestIndex);
  54. Nturns = round(ga_temp*Ne/pi);
  55. fprintf('# turns changed to: %d\n', Nturns);
  56. ga = Nturns*pi/Ne;
  57. fprintf('rotation set to: %d (ga = %d) \n', ga,ga_temp);
  58. fprintf('#Echoes set to: %d (Nechoes = %d) \n', Ne, Nechoes);
  59. Nechoes=Ne;
  60. case default
  61. ga = RotEcho;
  62. end
  64. %% Define k-space
  65. gx0 = [];
  66. gy0 = [];
  67. gz = [];
  68. ad = [];
  70. %% Define Rotation between z
  71. dtheta = ga;
  73. %% Build gradients
  75. % define radial line
  76. [gr0,nr,nf,~,ad0] = dotrap3((nx-1)/fovx/gambar,gmax,dgdtmax*0.8,dt);
  77. ngr0 = length(gr0);
  78. % define k space parameters
  79. k_max = gambar*sum(gr0)*dt/2;
  80. k_rot = 2*sin(dtheta/2)*k_max;
  81. k_z = 0;
  82. k_blip_sum_back = sqrt(k_rot^2+(k_z)^2);
  83. % Generate Gradients
  84. if dtheta ~= 0
  85. gb = dotrap2(k_blip_sum_back/gambar,gmax,dgdtmax,dt)/k_blip_sum_back*k_rot; %rotation blip
  86. gzb = [];
  87. gzb_back = [];
  88. else
  89. gb = 0;
  90. gzb = 0;
  91. gzb_back = 0;
  92. end
  94. ext=[nr (length(gb))/2 (length(gzb)/2) (length(gzb_back)/2)];
  95. [npext,inpext] = max(ext);
  96. switch inpext
  97. case 1
  98. gr1 = gr0;
  99. ad1 = ad0;
  100. ngr1 = length(gr1);
  101. gb1 = [zeros(1,floor(npext-ext(2))) gb zeros(1,ceil(npext-ext(2)))];
  102. gzb1 = [zeros(1,floor(npext-ext(3))) gzb zeros(1,ceil(npext-ext(3)))];
  103. gzb_back1 = [zeros(1,floor(npext-ext(4))) gzb_back zeros(1,ceil(npext-ext(4)))];
  104. ngbmed= floor(length(gb1)/2);
  105. ngbrest=length(gb1)-ngbmed;
  106. case 2
  107. gr1 = [zeros(1,npext-ext(1)) gr0 zeros(1,npext-ext(1))];
  108. ad1 = [zeros(1,npext-ext(1)) ad0 zeros(1,npext-ext(1))];
  109. ngr1 = length(gr1);
  110. gb1 = gb;
  111. gzb1 = [zeros(1,floor(npext-ext(3))) gzb zeros(1,ceil(npext-ext(3)))];
  112. gzb_back1 = [zeros(1,floor(npext-ext(4))) gzb_back zeros(1,ceil(npext-ext(4)))];
  113. ngbmed= floor(length(gb1)/2);
  114. ngbrest=length(gb1)-ngbmed;
  115. case 3
  116. gr1 = [zeros(1,npext-ext(1)) gr0 zeros(1,npext-ext(1))];
  117. ad1 = [zeros(1,npext-ext(1)) ad0 zeros(1,npext-ext(1))];
  118. ngr1 = length(gr1);
  119. gb1 = [zeros(1,floor(npext-ext(2))) gb zeros(1,ceil(npext-ext(2)))];
  120. gzb1 = gzb;
  121. gzb_back1 = gzb_back;
  122. ngbmed= floor(length(gb1)/2);
  123. ngbrest=length(gb1)-ngbmed;
  124. end
  125. % Combine Gradients
  126. beta = ((1:NechoLines)-2)*dtheta+dtheta/2;
  127. for z = 1:NechoLines
  128. theta = (z-1)*dtheta;
  129. if z == 1
  130. gx0 = [gx0(1:end) gr1(1:end-ngbmed) gr1(1+end-ngbmed:end)-sin(beta(z+1))*gb1(1:ngbmed)];
  131. gy0 = [gy0(1:end) 0*gr1(1:end-ngbmed) 0*gr1(1+end-ngbmed:end)+cos(beta(z+1))*gb1(1:ngbmed)];
  132. gz = [gz(1:end) 0*gr1(1:end-ngbmed) gzb1(1:ngbmed)];
  133. ad = [ad(1:end) ad1];
  135. elseif z == NechoLines
  136. gx0 = [gx0(1:end) -sin(beta(z))*gb1(1+ngbmed:end)*(-1)^(z)+cos(theta)*gr1(1:ngbrest)*(-1)^(z-1) cos(theta)*gr1(1+ngbrest:end)*(-1)^(z-1)];
  137. gy0 = [gy0(1:end) cos(beta(z))*gb1(1+ngbmed:end)*(-1)^(z)+sin(theta)*gr1(1:ngbrest)*(-1)^(z-1) sin(theta)*gr1(1+ngbrest:end)*(-1)^(z-1)];
  138. gz = [gz(1:end) gzb1(1+ngbmed:end) 0*gr1(1+ngbrest:end)];
  139. ad = [ad(1:end) ad1];
  140. else
  141. gx0 = [gx0(1:end) -sin(beta(z))*gb1(1+ngbmed:end)*(-1)^(z)+cos(theta)*gr1(1:ngbrest)*(-1)^(z-1) cos(theta)*gr1(1+ngbrest:end-ngbmed)*(-1)^(z-1) -sin(beta(z+1))*gb1(1:ngbmed)*(-1)^(z-1)+cos(theta)*gr1(end-ngbmed+1:end)*(-1)^(z-1)];
  142. gy0 = [gy0(1:end) cos(beta(z))*gb1(1+ngbmed:end)*(-1)^(z)+sin(theta)*gr1(1:ngbrest)*(-1)^(z-1) sin(theta)*gr1(1+ngbrest:end-ngbmed)*(-1)^(z-1) cos(beta(z+1))*gb1(1:ngbmed)*(-1)^(z-1)+sin(theta)*gr1(end-ngbmed+1:end)*(-1)^(z-1)];
  143. gz = [gz(1:end) gzb1(1+ngbmed:end) 0*gr1(1+ngbrest:end-ngbmed) -gzb_back1(1:ngbmed)];
  144. ad = [ad(1:end) ad1];
  145. end
  146. kxx1=cumsum(gx0);
  147. kyy1=cumsum(gy0);
  148. end
  150. %% Make pre-winders
  151. k_all = k_max;
  152. gw = -dotrap2(k_all/gambar,gmax,dgdtmax,dt);
  153. gxw = gw;
  154. gyw = 0*gw;
  155. gzw = 0*gw;
  156. k_max = gambar*sum(gr0(find(ad0)))*dt/2;
  158. %% build final waveforms
  159. ad = [0*gxw(1:end-1) ad];
  160. gx = [gxw(1:end-1) gx0];
  161. gy = [gyw(1:end-1) gy0];
  162. gz = [gzw(1:end-1) gz];
  163. npnts = length(gx)
  165. %% Check k-space
  166. kxx = cumsum(gx)*gambar*dt;
  167. kyy = cumsum(gy)*gambar*dt;
  168. kzz = cumsum(gz)*gambar*dt;
  169. ind = find(ad);
  171. figure(1),subplot(1,1,1);
  172. plot3(kxx(ind),kyy(ind),kzz(ind),'.b','MarkerSize',8);
  173. xlabel('k_x (1/cm)','FontSize',16);
  174. ylabel('k_y (1/cm)','FontSize',16);
  175. zlabel('k_z (1/cm)','FontSize',16);
  176. axis('tight');
  177. set(gca,'FontSize',20);
  179. view(-100,10);
  180. axis('on')
  182. %% Plot the pulse %%
  183. figure(2)
  184. timevec = (1:npnts)*dt*1000;
  185. subplot(4,1,1)
  186. plot(timevec,gx,'-b','LineWidth',2);
  187. axis([0 timevec((npnts)) -1.1*gmax 1.1*gmax]);
  188. ylabel('G_x (g/cm)','FontSize',16);
  189. set(gca,'FontSize',22);
  191. subplot(4,1,2)
  192. plot(timevec,gy,'-b','LineWidth',2);
  193. axis([0 timevec((npnts)) -1.1*gmax 1.1*gmax]);
  194. ylabel('G_y (g/cm)','FontSize',16);
  195. set(gca,'FontSize',22);
  197. subplot(4,1,3)
  198. plot(timevec,gz,'-b','LineWidth',2);
  199. axis([0 timevec((npnts)) -1.1*gmax/10 1.1*gmax/10]);
  200. ylabel('G_z (g/cm)','FontSize',16);
  201. set(gca,'FontSize',22);
  203. subplot(4,1,4)
  204. plot(timevec,ad,'-b','LineWidth',2);
  205. axis([0 timevec((npnts)) -gmax gmax]);
  206. ylabel('AD','FontSize',20);
  207. xlabel('Time (ms)','FontSize',16);
  208. set(gca,'FontSize',22);
  210. %% multi-shot simulation (golden angle)
  211. if do_simulation
  212. gax = floor(Nrot*ga1/pi)*pi/Nrot;
  213. kxrot=[];
  214. kyrot=[];
  215. kzrot=[];
  216. for l=0:Nrot-1
  217. kxrot=[kxrot kxx(ind)*cos(gax*l)+kyy(ind)*sin(gax*l)];
  218. kyrot=[kyrot kyy(ind)*cos(gax*l)-kxx(ind)*sin(gax*l)];
  219. kzrot=[kzrot kzz(ind)];
  220. end
  222. figure(20),subplot(1,1,1);
  223. plot3(kxrot,kyrot,kzrot,'.','color','[0 0 0.8]', 'MarkerSize',8);
  224. xlabel('k_x (1/cm)','FontSize',12);
  225. ylabel('k_y (1/cm)','FontSize',12);
  226. zlabel('k_z (1/cm)','FontSize',12);
  227. axis('tight');
  228. set(gca,'FontSize',20);
  229. axis([-max(kxx) max(kxx) -max(kxx) max(kxx)]);
  230. view(-100,10);
  231. axis('off')
  232. end
  234. %% Check pulse%%
  235. disp('effective_resolution:');
  236. nx_eff=floor(k_max*2*fovx)
  237. fprintf('Nechoes: %d; ', Nechoes);
  238. fprintf('phi: %d; ', ga*180/pi);
  239. fprintf('fovx: %d; ', fovx);
  241. if max(sqrt(gx.^2+gy.^2+gz.^2))>gmax
  242. display ('----------------------ERROR: MAX GRADIENT POWER VIOLATION----------------------');
  243. display (max(sqrt(gx.^2+gy.^2+gz.^2)));
  244. end
  245. if dgdtmax<max(diff(sqrt(gx.^2+gy.^2+gz.^2))/dt)
  246. display ('-------------------------ERROR: MAX SLEW RATE VIOLATION------------------------');
  247. display (max(diff(sqrt(gx.^2+gy.^2+gz.^2))/dt));
  248. end
  249. check=find(isnan(gx+gy+gz+ad));
  250. if check
  251. display ('-------------------------ERROR: NaN in Pulsevector------------------------');
  252. return
  253. end
  254. display ('----------------------Sampling efficiency (ADC ON/length)----------------------');
  255. display (length(find(ad))/length(ad));
  256. %%
  257. if write_files
  258. %% write files %%
  259. fname1=['sstraj.', num2str(npnts)];
  260. fid=fopen([fname1,'.',num2str(trajnum),'.gx'],'w');
  261. fprintf(fid, '%f\n', gx/gscale);
  262. fclose(fid);
  263. fid=fopen([fname1,'.',num2str(trajnum),'.gy'],'w');
  264. fprintf(fid, '%f\n', gy/gscale);
  265. fclose(fid);
  266. fid=fopen([fname1,'.',num2str(trajnum),'.gz'],'w');
  267. fprintf(fid, '%f\n', -gz/gscale);
  268. fclose(fid);
  269. fid=fopen([fname1,'.',num2str(trajnum),'.ad'],'w');
  270. fprintf(fid, '%f\n', ad);
  271. fclose(fid);
  273. %% write header
  274. fid=fopen([fname1,'.',num2str(trajnum),'.hdr'],'w');
  275. fprintf(fid,'fovxy=%f trajnum=%d trajectory_type=%d do_buff=%d calimode=%d GreRotType=%d rot=%d SeqOpt=%d n=%d Nturns=%d do_simulation=%d Nrot=%d kzflip=%d w=%f Nblips=%d nxy=%d nz=%d Nechoes=%d SpiralTwist=%d fblip=%d dgdtmax=%d gmax=%d dt=%e', fovx, trajnum, trajectory_type, do_buff, calimode, GreRotType, rot, SeqOpt, n, Nturns, do_simulation, Nrot, kzflip, w, Nblips, nx, nz, Nechoes, SpiralTwist, fblip, dgdtmax, gmax, dt);
  276. fprintf(fid,' fovxy=%f nxy=%d fovz=%f nz=%d rxy=%d rz=%d gmax=%d',fovx,nx_eff,fovzz,Nblips,NechoLines,Nblips, gmax);
  277. fclose(fid);
  278. end

mk2DrEPItraj.m at commit 5e64e4e, no license · at the source

Overview

Authors: Christoph A Rettenmeier1, Zidan Yu1, Krystalyn Edwards‐Calma1, Kai Tobias Block2, V Andrew Stenger1
ORCID iDs: Christoph A Rettenmeier, Krystalyn Edwards‐Calma
  1. Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
  2. Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
Institutions: University of Hawaiʻi at Mānoa (United States); University of Hawaii System (United States); New York University (United States)
Journal: Magnetic resonance in medicine, volume 96, issue 3, pages 1245-1260
Dates: received 8 January 2026; accepted 4 May 2026; published online 17 May 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/mrm.70433 · PMID 42143757 · PMCID PMC13327459 · OpenAlex W7161496858
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), clinical / translational (subfield)
Methods: Smoothing, state filtering, decompositions, Spectral & time-frequency, fMRI & imaging, Connectivity
Keywords: BOLD fMRI, fast multi‐contrast imaging, KWIC, multi‐echo, radial EPI, single‐shot
MeSH: Brain*, Echo-Planar Imaging*, 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 (P20GM139753, R01EB028627)
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Purpose: To develop a novel single‐shot radial echo planar imaging (ss‐rEPI) technique for rapid, distortion‐free brain imaging in functional MRI experiments.

Methods: Radial multi‐gradient echo (radial mGRE) data were acquired on a 3T clinical scanner using a 2D ss‐rEPI readout with small golden‐angle rotations between echoes. Images were reconstructed using an iterative conjugate‐gradient method incorporating coil sensitivities, B 0 field inhomogeneities, transverse relaxation, and field‐drift correction to account for signal inconsistencies in the extended mGRE readout. Additional k‐space‐weighted image contrast (KWIC) filtering prior to reconstruction helped reduce model mismatches at low spatial frequencies. Single‐shot rEPI image quality, contrast, and distortion were assessed against multi‐shot radial mGRE reference data. Retrospective adjustment of the KWIC filter and target TE in the reconstruction allowed the generation of multiple T 2*weighted and phase contrast images from a single ss‐rEPI scan, enabling quantitative T 2* mapping and QSM. Visual BOLD fMRI experiments were conducted and evaluated against Cartesian EPI measurements.

Results: Twenty‐four 3 mm thick slices of distortion‐free, multi‐contrast brain images were obtained at 2 × 2 and 1.5 mm2 (ramp‐sampled) in‐plane resolution with an acquisition time of under 1.7 s. In multi‐session fMRI experiments on two subjects, ss‐rEPI demonstrated BOLD activation in the visual cortex comparable to standard EPI while also enabling functional T 2* measurements.

Conclusion: Single‐shot rEPI enables rapid, distortion‐free 2D multi‐contrast brain imaging, offering a promising alternative to Cartesian EPI. Accurate ∆B 0 modeling is critical for ss‐rEPI performance. Advanced reconstruction techniques and self‐calibration methods could further enhance its speed, performance, and applicability across diverse MRI techniques.

Reproduced under the paper's license (CC BY-NC), 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.

crettenm/ss-rEPI

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 5e64e4e9dc33ce4a0b27adb59033c982f90ee55f, 11 May 2026
Languages: MATLAB (278), C (20), C/C++ (5)
Size: 342 files, 303 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: Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
304 files
At the source: github.com/crettenm/ss-rEPI

Zenodo 19520791

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
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 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.19520791

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;
  • 303 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

No dataset and no data link were found in the paper.

Data Availability Statement

The reconstruction code, raw k‐space data, and main scripts are available at https://github.com/crettenm/ss‐rEPI (https://github.com/crettenm/ss-rEPI), and Zenodo.org (https://doi.org/10.5281/zenodo.19520791).

Reproduced under the paper's license (CC BY-NC), 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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 10 MeSH terms, 1 funder, 57 references.

Cite

This paper

Rettenmeier, C. A., Yu, Z., Edwards‐Calma, K., Block, K. T., & Stenger, V. A. (2026). Single-Shot 2D Radial Echo Planar Imaging for Functional MRI. Magnetic resonance in medicine, 96(3), 1245-1260. https://doi.org/10.1002/mrm.70433

BibTeX

@article{rettenmeier2026single,
author = {Rettenmeier, Christoph A and Yu, Zidan and Edwards‐Calma, Krystalyn and Block, Kai Tobias and Stenger, V Andrew},
title = {{Single-Shot 2D Radial Echo Planar Imaging for Functional MRI}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = may,
volume = {96},
number = {3},
pages = {1245--1260},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70433},
url = {https://doi.org/10.1002/mrm.70433},
pmid = {42143757},
pmcid = {PMC13327459}
}

RIS

TY - JOUR
AU - Rettenmeier, Christoph A
AU - Yu, Zidan
AU - Edwards‐Calma, Krystalyn
AU - Block, Kai Tobias
AU - Stenger, V Andrew
TI - Single-Shot 2D Radial Echo Planar Imaging for Functional MRI
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/05/17
VL - 96
IS - 3
SP - 1245
EP - 1260
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70433
UR - https://doi.org/10.1002/mrm.70433
LA - en
ER -

CSL-JSON

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"id": "10.1002/mrm.70433",
"type": "article-journal",
"title": "Single-Shot 2D Radial Echo Planar Imaging for Functional MRI",
"container-title": "Magnetic resonance in medicine",
"author": [
{
"family": "Rettenmeier",
"given": "Christoph A"
},
{
"family": "Yu",
"given": "Zidan"
},
{
"family": "Edwards‐Calma",
"given": "Krystalyn"
},
{
"family": "Block",
"given": "Kai Tobias"
},
{
"family": "Stenger",
"given": "V Andrew"
}
],
"container-title-short": "Magn Reson Med",
"volume": "96",
"issue": "3",
"page": "1245-1260",
"DOI": "10.1002/mrm.70433",
"PMID": "42143757",
"PMCID": "PMC13327459",
"ISSN": "0740-3194",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/mrm.70433",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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17
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]
}
}

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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/mrm.70333 [code]
Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.
Journal: Magnetic resonance in medicine
In common: 7 references
[2] doi:10.1002/mrm.70439 [code]
Group-Patch Joint Compression: Compressing Dynamic B&lt;sub&gt;0&lt;/sub&gt; and Static RF Spatial Modulations Across k-Space Subregion Groups for Highly Accelerated MRI.
Journal: Magnetic resonance in medicine
In common: 5 references
[3] doi:10.1038/s41598-026-58377-2 [code]
A novel distortion-matched anatomical imaging sequence for high-fidelity functional mapping in submillimeter-resolution fMRI.
Journal: Scientific reports
In common: fMRI, 4 references
[4] doi:10.1002/mrm.70469 [code]
Laterally Oscillating Trajectory for Undersampling Slices: LOTUS.
Journal: Magnetic resonance in medicine
In common: 4 references
[5] doi:10.7554/elife.92805 [code]
Brain-wide mapping of layer-specific functional connectivity in the human cortex at 3T using draining-vein-suppressed fMRI.
Journal: eLife
In common: Signal Processing Toolbox, fMRI, 3 references
[6] doi:10.1162/imag.a.1247 [code]
Evaluating BOLD functional MRI biophysical simulation approaches: Impact of vascular geometry, magnetic field calculations, and water diffusion models.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, 3 references
[7] doi:10.1002/nbm.70305 [code]
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
In common: Signal Processing Toolbox, 2 references
[8] doi:10.1162/imag.a.1354 [code]
Development of a 24-channel 3T phased-array coil for fMRI in awake monkeys: Mitigating spatiotemporal artifacts in ferumoxytol-weighted functional connectivity estimation.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fMRI, 3 references
[9] doi:10.1111/ejn.70666
Resting State Functional Connectivity Among Cortical and Subcortical Grey Matter Structures Can Distinguish Fallers in Parkinson's Disease.
Journal: The European journal of neuroscience
In common: fMRI, 3 references
[10] doi:10.1038/s41467-026-72934-3 [code]
Multi-focal ultrasound neuromodulation to the dorsal anterior cingulate cortex disrupts behavioural and neural pain processing.
Journal: Nature communications
In common: fMRI, 3 references

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