OSCR

Meningeal CSF transport varies across parasagittal dura subregions with age in humans.

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.

The 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Imaging ↔ ASL_main.m, lines 42–103 · score 0.60 · SLIP series, 3000 ms, volumes, M0, slices, TI
  2. [2] § Results ↔ asl_subroutines/aslFitParams.m, the whole file · a weak match · score 0.55 · full width, Peak heights, FWHM, AUC, TTP, curve
  3. [3] § Results ↔ ASL_main.m, lines 42–103 · score 0.51 · subtraction maps, 3000 ms, zoomed, overlays, colormaps, M0
  4. [4] § Results ↔ asl_subroutines/aslFit.m, lines 209–293 · score 0.51 · bi component, TTPG, R2, FWHM, AUC, PH

Paper

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The authors' code

MATLAB · 719 lines · 24 KB · MIT · 2 matches

  1. %% ========================================================================
  2. % Top level code for perfusion anlysis
  3. %
  4. %==========================================================================
  5. %
  6. % 05/2025 - VM for Canon ([email hidden]) v2.0
  7. %
  8. %==========================================================================
  9. %
  10. % expected folder strucutre:
  11. %
  12. % ---|Subject
  13. % |---pre/post/postN folders
  14. % required|--- roi [dicom folder] + (Horos roi *.csv file)
  15. % required|--- tslip [dicom folderS]
  16. % optional|--- Control [dicom folderS] if opp. tag (MT cancel)
  17. % optional|--- M0 [dicom folder] optional
  18. %
  19. % use ExportRoi Plugin from:
  20. % https://horosproject.org/horos-content/plugins/horos/ExportROIs/
  21. %
  22. %% ========================================================================
  23. clear all
  24. clc
  26. tic
  28. start_directory=pwd;
  30. % SUBJECTS list
  31. list = dir3();
  33. if exist('progress.mat','file')
  34. load('progress.mat')
  35. if ii>numel(list)
  36. disp('All data processed!');
  37. return
  38. end
  40. else
  42. %% Define parameters
  43. %--------------------------------------------------------------------------
  44. param.acq_type = 'bright'; % bright (NonSelect+Select); dark (Select)
  45. %param.acq_dim = '2d'; % 2d (single T-SLIP series with multiple TI)
  46. param.acq_dim = '3d'; % 3d (every TI separate series)
  47. %param.TI = [1000, 500];
  48. param.dicomScale = true; % MUST BE ON
  49. param.perVoxelCalc = true; % more prone to error
  50. param.T1 = 2000; % fixed CSF T1 for bi-component fit:
  51. % Blood use 1500, CSF use 3000
  52. param.registration = 'all'; % rigid body registration to M0:
  53. % extreme (myocardium)
  54. % all all images to M0
  55. % auto automatic
  56. % off
  57. %------acquisition with fixed RO-------------------------------------------
  58. param.fixedRO = false; % BBIRprep WIP1
  59. %param.bpm = 70; % heart rate
  60. %--------------------------------------------------------------------------
  61. param.filter_median3D = true; % for 3D volume median filter across slices
  62. param.filter_median2D = true; % additional median for slice of interest
  63. param.filter_sd = true; % doubleSD window for per ROI average
  64. param.noiseM0 = false; % dont normalize to noise
  65. param.absValue = false; % subtraction as absolute value
  66. param.nullPointFilter = [false,750];% remove short TI [bool, filter]
  67. param.filter_lowpass = 'lowPass'; % 'lowPass' 'movinAver' 'off'
  68. param.fitplot = {'bi','gm','biC'}; % 'gkm','gm', 'gmf', 'gs', 'bi' list them in cell
  69. %--------------------------------------------------------------------------
  70. % average across slices in 3D volume ONLY FOR LARGE PERFUSION VOLUME
  71. param.averageSlices = false;
  72. %--------------------------------------------------------------------------
  73. % to print TEs on colormap, use "other" for SeriesID
  74. param.dataType = 'regular';
  75. % use false for NIH (Arial font) and true (latex) for publications
  76. param.latex = true;
  77. %--------------------------------------------------------------------------
  78. % colormaps
  79. % varibles with two values 1 - plot/dont plot; 2 - zoom to roi / full FOV
  80. param.visible = false; % dont show figures, plot silently
  81. param.allslices = false; % if 3D colormaps for all slices
  82. param.paleteview = [false,false]; % does all TIs
  83. param.subtractionMap = [false,false]; % abs(ON-OFF)
  84. param.montageALL = [false,false]; % grayscale: ON, OFF, Control
  85. param.sir2Dcmap = [false,true]; % 2D SIR
  86. param.sir2DcmapFull = false; % to overlay entire FOV
  87. param.contrastNorm = true; % enchance contrast, not true Signal
  88. %--------------------------------------------------------------------------
  89. param.SIRLim = 100; %default limit for plots
  90. param.SIRClim = 100; %default limit for colormaps
  91. param.TILim = 5000; %custom x axis limits
  92. %--------------------------------------------------------------------------
  93. % only for advanced user, keep these as is
  94. param.nonAlternate = false; % if control is acquired sep.
  95. param.advancedGKM=false; % advanced GKM Fit
  98. % Check parameters
  99. param = tslipParamCheck(param);
  101. if isfield(param,'TI')
  102. param.TI0=param.TI; % for 2D
  103. end
  105. %% PROCESSING
  106. %==========================================================================
  108. % structure with results: appended at each subject
  109. ResultM = [];
  110. ResultP = [];
  112. ii=1;
  113. jj=1;
  116. end
  118. %************************************************************ LOOP: SUBJECT
  119. for i=ii:numel(list)
  121. cd(list(i,1).name)
  123. % GROUP/CATEGORY list (such as activity/pre-post/etc)
  124. list2 = dir3();
  128. %**************************************************LOOP: GROUP/CATEGORY
  129. for j=jj:numel(list2)
  131. if jj==1
  132. RM=[];
  133. RP=[];
  134. end
  136. % Subject/Study info
  137. Subject.ID = list(i,1).name;
  138. Subject.study = list2(j,1).name;
  139. q=1; % study results index
  141. cd(list2(j,1).name)
  143. %----------------------------------------------------- START: read data
  144. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: read ROI
  145. cd roi
  146. csv=dir('*.csv');
  147. fname = [list(i,1).name,' ',list2(j,1).name,];
  148. ROI = horos2matlabTSLIP(csv,fname,true,param);
  150. % Find the indices of all 1s in the array create combined Full
  151. ROI.maskF = sum(~isnan(ROI.maskIm), 3) > 0;
  152. [row, col] = find(ROI.maskF == 1);
  153. % Find the minimum and maximum row and column indices
  154. n=10; %padding
  155. ROI.mtop = max(min(row) - n, 1);
  156. ROI.mbottom = min(max(row) + n, size(ROI.maskF, 1));
  157. ROI.mleft = max(min(col) - n, 1);
  158. ROI.mright = min(max(col) + n, size(ROI.maskF, 2));
  160. cd .. % out of 'roi'
  162. % do MICO for MPRAGE or T1w (MPRAGE is priority)
  163. if isfolder('MPRAGE')
  164. cd 'MPRAGE'
  165. MPRAGE2FASE('MPRAGE','FASE2D')
  166. seriesPath=fullfile(pwd,'Image4Segmentation');
  167. evalc('R = aslMICO(seriesPath, param, true);');
  168. if ~isempty(R)
  169. ROI = mergeASLroi(ROI, R);
  170. end
  172. cd ..
  174. elseif ~isfolder('MPRAGE') && isfolder('T1w')
  175. seriesPath=fullfile(pwd,'T1w');
  176. evalc('R = aslMICO(seriesPath, param, true);');
  177. if ~isempty(R)
  178. ROI = mergeASLroi(ROI, R);
  179. end
  183. end
  186. clearvars csv fname n row col
  187. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: read ROI
  189. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: read M0
  190. if exist('M0','dir')
  191. [M0,VM0]=readTslip('M0',ROI,param);
  192. if ~isempty(VM0) && strcmp(param.acq_dim,'3d') %only created for 3d
  193. V.M0 = VM0.On;
  194. end
  195. if isempty(VM0) && strcmp(param.acq_dim,'2d') %only created for 3d
  196. M0=M0(:,:,1);
  197. end
  198. % M0 should only have single series
  199. if isfield(M0,'Ctrl') && isfield(M0,'Ctrl_roi') ...
  200. && ~isempty([M0.Ctrl]) && ~isempty(Ctrl_roi)
  202. M0.On = M0.Ctrl;
  203. M0.On_roi = M0.Ctrl_roi;
  204. M0=rmfield(M0,'Ctrl');
  205. M0=rmfield(M0,'Ctrl_roi');
  206. end
  207. clearvars VM0
  208. end
  209. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: read M0
  211. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: read CONTROL
  212. if exist('Control','dir')
  213. [Control,VC]=readTslip('control',ROI,param);
  214. if ~isempty(VC)&& strcmp(param.acq_dim,'3d') %only created for 3d
  215. V.Ctrl=VC.On;
  216. end
  217. % control should only have ON series
  218. if isfield(Control,'Ctrl') && isfield(Control,'Ctrl_roi')
  219. Control=rmfield(Control,'Ctrl');
  220. Control=rmfield(Control,'Ctrl_roi');
  221. end
  222. clearvars VC
  223. end
  224. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: read Control
  226. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: read TSLIP
  227. if exist('tslip','dir')
  228. [TSlip,VT,param]=readTslip('tslip',ROI,param);
  229. if ~isempty(VT)&& strcmp(param.acq_dim,'3d') %only created for 3d
  230. V.On=VT.On;
  231. if isfield(VT,'Ctrl')
  232. V.Ctrl=VT.Ctrl;
  233. end
  234. if ~isfield(V,'M0') && strcmp(param.acq_type,'bright') && ...
  235. strcmp(param.acq_dim,'3d') && ~isfield(VT,'Ctrl')
  236. V.M0 = V.Ctrl(:,:,:,end);
  237. param.nonAlternate=true;
  238. elseif ~isfield(V,'M0') && strcmp(param.acq_type,'dark') && ...
  239. strcmp(param.acq_dim,'3d') && ~isfield(VT,'Ctrl')
  240. V.M0 = V.Ctrl;
  241. param.nonAlternate=true;
  242. elseif ~isfield(V,'M0') && strcmp(param.acq_type,'bright') && ...
  243. strcmp(param.acq_dim,'3d')
  244. V.M0 = VT.Ctrl(:,:,:,end);
  245. elseif ~isfield(V,'M0') && strcmp(param.acq_type,'dark') && ...
  246. strcmp(param.acq_dim,'3d')
  247. V.M0 = VT.Ctrl;
  248. end
  249. end
  250. clearvars VT
  251. else
  252. error('Data should have tslip folder');
  253. end
  254. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: read TSLIP
  257. %------------------------------------------------------- END: read data
  258. nTIs=numel(TSlip);
  261. %------------------------------------------------ START: ararys for SIR
  262. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: denominator
  263. if exist('M0','var')
  264. A.M0=TslipStuct2Array(M0,'On',param.dicomScale);
  265. m0=squeeze(TslipStuct2Array(M0,'On_roi',...
  266. param.dicomScale));
  267. elseif ~exist('M0','var') && strcmp(param.acq_dim,'3d') ...
  268. && ~param.nonAlternate
  269. A.M0=repmat(TslipStuct2Array(TSlip(end),'Ctrl',param.dicomScale),[1,1,nTIs]);
  270. m0=squeeze(TslipStuct2Array(TSlip(end),'Ctrl_roi',...
  271. param.dicomScale));
  272. elseif ~exist('M0','var') && strcmp(param.acq_dim,'3d') ...
  273. && param.nonAlternate
  274. A.M0=repmat(TslipStuct2Array(Control(end),'On',param.dicomScale),[1,1,nTIs]);
  275. m0=squeeze(TslipStuct2Array(TSlip(end),'On_roi',...
  276. param.dicomScale));
  277. elseif ~exist('M0','var') && strcmp(param.acq_dim,'2d')
  278. A.M0=repmat(TslipStuct2Array(Control(end),'On',param.dicomScale),[1,1,nTIs]);
  279. m0=squeeze(TslipStuct2Array(TSlip(end),'On_roi',...
  280. param.dicomScale));
  281. end
  282. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: denominator
  284. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: On
  285. A.On=TslipStuct2Array(TSlip,'On',param.dicomScale);
  286. on=squeeze(TslipStuct2Array(TSlip,'On_roi',param.dicomScale));
  287. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: On
  289. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: Control
  290. if exist('Control','var')
  291. A.Ctrl=TslipStuct2Array(Control,'On',param.dicomScale);
  292. ctrl=squeeze(TslipStuct2Array(Control,'On_roi',param.dicomScale));
  293. else
  294. A.Ctrl=TslipStuct2Array(TSlip,'Ctrl',param.dicomScale);
  295. ctrl=squeeze(TslipStuct2Array(TSlip,'Ctrl_roi',...
  296. param.dicomScale));
  297. end
  298. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: Control
  300. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: check m0
  301. if size(m0,2)~=size(on,2)
  302. m0=repmat(m0,[1,size(on,2)]);
  303. end
  304. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: check m0
  307. %-------------------------------------------------- END: ararys for SIR
  309. %%-------------------------------------check if pre-null point to be removed
  311. % check dims of M0 and m0 cause sometimes they are acquired seperat;y
  312. % with less dT
  314. if size(A.M0,3)>1 && size(A.M0,3) ~= size(A.On,3)
  315. A.M0=A.M0(:,:,1);
  316. end
  319. if min(size(m0))>1
  320. if size(m0, 2) > 1 && size(m0,2) ~= size(on,2)
  321. m0=m0(:,1);
  322. end
  323. else
  324. if size(m0, 1) > 1 && size(m0,1) ~= size(on,1)
  325. m0=m0(1);
  326. end
  327. end
  330. if param.nullPointFilter(1)
  332. % Get TI values from the structure and find indices below the threshold
  333. TI_values = [TSlip.TI];
  334. idx = TI_values < param.nullPointFilter(2); % Logical indexing is more efficient than `find`
  337. % Conditional checks before modifying arrays to avoid errors
  338. if exist('Control','var')
  339. Control(idx) = [];
  340. end
  342. if exist('M0', 'var') && size(M0, 2) > 1 && size(M0, 2)== size(TSlip, 2)
  343. M0(idx) = [];
  344. end
  346. % Remove entries based on identified indices
  347. TSlip(idx) = [];
  349. if min(size(m0))>1
  350. if size(m0, 2) > 1 && size(m0,2) == size(on,2)
  351. m0(:, idx) = [];
  352. else
  353. m0=m0(:,1);
  354. end
  355. elseif size(ROI.name,1)>1 && size(m0,2) == 1
  357. else
  358. if size(m0, 1) > 1 && size(m0,1) == size(on,1)
  359. m0(idx) = [];
  360. else
  361. m0=m0(1);
  362. end
  363. end
  366. if size(size(on))<2
  367. on(idx) = [];
  368. ctrl(idx) = [];
  369. else
  370. on(:, idx) = [];
  371. ctrl(:, idx) = [];
  372. end
  374. if isfield(A, 'M0') && size(A.M0,3)==size(A.On,3)
  375. A.M0(:, :, idx) = [];
  376. else
  378. end
  380. % Update fields within structure A
  381. A.On(:, :, idx) = [];
  382. A.Ctrl(:, :, idx) = [];
  385. % Check acquisition dimension and update variables accordingly
  386. if strcmp(param.acq_dim, '3d')
  387. V.On(:, :, :, idx) = [];
  388. V.Ctrl(:, :, :, idx) = [];
  390. if isfield(V, 'M0') && size(V.M0, 4) > 1
  391. V.M0(:, :, :, idx) = [];
  392. end
  393. elseif strcmp(param.acq_dim, '2d')
  394. param.TI(1) = TI_values(1);
  395. end
  396. end
  398. nTIs=max(size([TSlip.TI]));
  400. clearvars idx TI_values
  401. %%-------------------------------------------------------------------------
  402. if ~strcmpi(param.registration,'off')
  403. A=ASL_imReg(A, param);
  404. end
  411. %-------------------------------------------------- START: get noise
  412. %masks for colormaps
  413. if strcmp(param.acq_dim,'3d') && ~isempty(V)
  414. ROI.mask=UTEsMask(mat2gray(max(V.Ctrl,[],[3,4])),0.02);
  415. if param.noiseM0
  416. ROI.noiseLevel = std(V.On(V.On.*abs(ROI.mask-1) ~= 0), 0,"all", "omitnan");
  417. temp = ones(size(V.On));
  418. temp(abs(V.On-V.Ctrl) < ROI.noiseLevel/sqrt(2)) = NaN;
  419. ROI.Noise=temp.*ROI.mask;
  420. end
  421. else
  422. ROI.mask=UTEsMask(mat2gray(max(A.Ctrl,[],[3,4])),0.02);
  423. if param.noiseM0
  424. ROI.noiseLevel = std(A.On(A.On.*abs(ROI.mask-1) ~= 0),0, "all", "omitnan");
  425. temp = ones(size(A.On));
  426. temp(abs(A.On-A.Ctrl) < ROI.noiseLevel/sqrt(2)) = NaN;
  427. ROI.Noise=temp.*ROI.mask;
  428. end
  429. end
  431. % mean recalcs with Noise mask
  432. if param.noiseM0
  433. on=squeeze(mean(permute(repmat(A.On.*ROI.Noise,...
  434. [1,1,1,size(ROI.maskIm,3)]),[1,2,4,3]).*repmat...
  435. (ROI.maskIm,[1,1,1,size(A.On,3)]),[1,2],'omitnan'));
  436. ctrl=squeeze(mean(permute(repmat(A.Ctrl.*ROI.Noise,...
  437. [1,1,1,size(ROI.maskIm,3)]),[1,2,4,3]).*repmat...
  438. (ROI.maskIm,[1,1,1,size(A.Ctrl,3)]),[1,2],'omitnan'));
  439. m0=squeeze(mean(permute(repmat(A.M0.*ROI.Noise,...
  440. [1,1,1,size(ROI.maskIm,3)]),[1,2,4,3]).*repmat...
  441. (ROI.maskIm,[1,1,1,size(A.M0,3)]),[1,2],'omitnan'));
  443. on(isnan(on))=0;
  444. ctrl(isnan(ctrl))=0;
  445. m0(isnan(m0))=0;
  447. on(isinf(on))=0;
  448. ctrl(isinf(ctrl))=0;
  449. m0(isinf(m0))=0;
  450. end
  453. clearvars temp
  454. %-------------------------------------------------- END: get noise
  457. %------------------------------------------------- START: calculate SIR
  458. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: full array
  459. if strcmp(param.acq_dim,'3d') && ~isempty(V)
  461. tmpOn = V.On;
  462. tmpCtrl = V.Ctrl;
  463. tmpM0 = V.M0;
  465. if param.noiseM0
  466. tmpM0=tmpM0.*ROI.Noise;
  467. end
  469. if param.filter_median3D
  470. tmpOn = arrayfun(@(i) medfilt3(tmpOn(:,:,:,i)), ...
  471. 1:size(tmpOn,4), 'UniformOutput', false);
  472. tmpOn = cat(4, tmpOn{:});
  473. tmpCtrl = arrayfun(@(i) medfilt3(tmpCtrl(:,:,:,i)), ...
  474. 1:size(tmpCtrl,4), 'UniformOutput', false);
  475. tmpCtrl = cat(4, tmpCtrl{:});
  476. tmpM0 = arrayfun(@(i) medfilt3(tmpM0(:,:,:,i)), ...
  477. 1:size(tmpM0,4), 'UniformOutput', false);
  478. tmpM0 = cat(4, tmpM0{:});
  479. end
  481. if param.absValue
  482. V.SUBT=abs(tmpOn-tmpCtrl);
  483. elseif ~param.absValue && strcmp(param.acq_type,'bright')
  484. V.SUBT=tmpOn-tmpCtrl;
  485. V.SUBT(V.SUBT<0)=0;
  486. else
  487. V.SUBT=tmpCtrl-tmpOn;
  488. V.SUBT(V.SUBT<0)=0;
  489. end
  491. if param.noiseM0
  492. V.SUBT=V.SUBT.*ROI.Noise;
  493. end
  495. V.SIR=V.SUBT./tmpM0;
  497. V.SUBT(isinf(V.SUBT))=NaN;
  498. V.SIR(isinf(V.SIR))=NaN;
  502. clearvars tmp tmpOn tmpCtrl tmpM0
  503. end
  506. if param.filter_median2D
  507. % Preallocate arrays with the same size as A.On
  508. tmpOn = zeros(size(A.On));
  509. tmpCtrl = tmpOn;
  511. % Check if A.M0 has multiple slices and preallocate accordingly
  512. if size(A.M0, 3) > 1
  513. tmpM0 = zeros(size(A.M0));
  514. end
  516. % Apply median filtering across all time points (nTIs)
  517. for t = 1:nTIs
  518. tmpOn(:, :, t) = medfilt2(A.On(:, :, t));
  519. tmpCtrl(:, :, t) = medfilt2(A.Ctrl(:, :, t));
  521. if size(A.M0, 3) > 1
  522. tmpM0(:, :, t) = medfilt2(A.M0(:, :, t));
  523. end
  524. end
  526. % Handle the case where A.M0 has only one slice
  527. if size(A.M0, 3) == 1
  528. tmpM0 = medfilt2(A.M0);
  529. end
  531. else
  532. tmpOn=A.On;
  533. tmpCtrl=A.Ctrl;
  534. tmpM0=A.M0;
  535. end
  537. if param.absValue
  538. A.SUBT=abs(tmpOn-tmpCtrl);
  539. elseif ~param.absValue && strcmp(param.acq_type,'bright')
  540. A.SUBT=tmpOn-tmpCtrl;
  541. A.SUBT(A.SUBT<0)=0;
  542. else
  543. A.SUBT=tmpCtrl-tmpOn;
  544. A.SUBT(A.SUBT<0)=0;
  545. end
  547. if param.noiseM0
  548. A.SUBT = A.SUBT.*ROI.Noise;
  549. end
  551. A.SIR=A.SUBT./tmpM0;
  553. A.SUBT(isinf(A.SUBT))=NaN;
  554. A.SIR(isinf(A.SIR))=NaN;
  557. clearvars tmpOn tmpCtrl tmpM0 t
  558. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: full array
  560. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Start: roi quantification
  562. if param.absValue
  563. SIR.mean=abs(on-ctrl)./m0;
  564. elseif ~param.absValue && strcmp(param.acq_type,'bright')
  565. temp=(on-ctrl)./m0;
  566. temp(temp<0)=0;
  567. SIR.mean=temp;
  568. else
  569. temp=(ctrl-on)./m0;
  570. temp(temp<0)=0;
  571. SIR.mean=temp;
  572. end
  573. clearvars on ctrl m0 tmp
  575. if param.perVoxelCalc
  576. pp=[];
  577. for m=1:size(ROI.maskIm,3)
  578. tmp=A.SIR.*repmat(ROI.maskIm(:,:,m),[1,1,nTIs]);
  579. tmp=(mean(tmp,[1,2],'omitnan'));
  580. pp=cat(1,pp,tmp);
  581. end
  582. SIR.pp=squeeze(pp);
  583. end
  585. clearvars tmp pp m
  586. %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ End: roi quantification
  587. %--------------------------------------------------- END: calculate SIR
  589. %----------------------------------------------------------- START: fit
  591. if param.fixedRO
  592. if param.perVoxelCalc
  593. [rM,rP]=aslFixedRO(SIR,[TSlip.TI],Subject,ROI,param,A.M0(:,:,1));
  594. else
  595. rM=aslFixedRO(SIR,[TSlip.TI],Subject,ROI,param,A.M0(:,:,1));
  596. end
  597. else
  598. if param.perVoxelCalc
  599. [rM,rP]=aslFit(SIR,[TSlip.TI],Subject,ROI,param,A.M0(:,:,1));
  600. else
  601. rM=aslFit(SIR,[TSlip.TI],Subject,ROI,param,A.M0(:,:,1));
  602. end
  604. end
  605. %------------------------------------------------------------- END: fit
  606. cd ..
  607. %----------------------------------------------------- START: colormaps
  608. mkdir('colormaps')
  609. cd('colormaps')
  611. if strcmp(param.acq_dim, '3d') && ~isempty(V) && param.allslices
  612. tempSUBT = mat2gray(V.SUBT .* ROI.mask);
  613. slice_indices = 1:size(V.On, 3);
  614. data_source = V;
  615. else
  616. slice_indices = ROI.slice_number;
  617. data_source = A;
  618. end
  620. for s = slice_indices
  621. if strcmp(param.acq_dim, '3d') && ~isempty(V) && param.allslices
  625. B = structfun(@(field) squeeze(field(:,:,s,:)), V,...
  626. 'UniformOutput', false);
  627. B.SUBT = squeeze(tempSUBT(:,:,s,:));
  628. else
  629. B = A;
  630. end
  632. operations = {
  633. 'palette', @paleteCMap, param.paleteview(1);
  634. 'montage', @tslipmontage, param.montageALL(1);
  635. 'subtraction', @(B, ROI, TI, s, param) perfusionCMap(B, ...
  636. ROI, TI, s, param, 'SUBT'), param.subtractionMap(1);
  637. 'SIR', @(B, ROI, TI, s, param) perfusionCMap(B, ROI, TI, ...
  638. s, param, 'SIR'), param.sir2Dcmap(1);
  639. };
  641. for x = 1:size(operations, 1)
  642. if operations{x, 3}
  643. mkdir(operations{x, 1});
  644. cd(operations{x, 1});
  645. operations{x, 2}(B, ROI, [TSlip.TI], s, param);
  646. cd ..;
  647. end
  648. end
  649. clearvars B x operations slice_indices s data_source
  650. end
  651. cd ..
  652. %------------------------------------------------------- END: colormaps
  654. if param.perVoxelCalc
  655. RM=[RM,rM];
  656. RP=[RP,rP];
  657. clearvars rM rP
  658. ii=i;
  659. jj=j+1;
  660. save([start_directory,'/progress.mat'],'ResultM','ResultP',...
  661. 'RM','RP','ii','jj','list','list2','param')
  662. else
  663. RM=[RM,rM];
  664. clearvars rM
  665. ii=i;
  666. jj=j+1;
  667. save([start_directory,'/progress.mat'],'ResultM','RM','ii','jj',...
  668. 'list','list2','param')
  669. end
  672. cd ..
  674. end
  676. %**************************************************LOOP: GROUP/CATEGORY
  678. if param.perVoxelCalc
  679. ResultM=[ResultM,RM];
  680. ResultP=[ResultP,RP];
  681. clearvars RM RP
  682. ii=i+1;
  683. jj=1;
  684. save([start_directory,'/progress.mat'],'ResultM','ResultP','ii','jj',...
  685. 'list','list2','param')
  686. else
  687. ResultM=[ResultM,RM];
  688. clearvars RM
  689. ii=i+1;
  690. jj=1;
  691. save([start_directory,'/progress.mat'],'ResultM','ii','jj',...
  692. 'list','list2','param')
  693. end
  694. cd ..
  696. end
  697. %************************************************************ LOOP: SUBJECT
  701. data_directory=pwd;
  703. folder_results = ['results_', char(datetime('today'), 'yyMMdd')];
  704. mkdir(folder_results);
  706. movePDFs(data_directory, folder_results)
  708. cd(folder_results)
  710. if param.perVoxelCalc
  711. aslResults2XLS(ResultM, 'ResultMean.xlsx',param)
  712. aslResults2XLS(ResultP, 'ResultPP.xlsx',param)
  713. save('results.mat', 'ResultM','ResultP')
  714. else
  715. aslResults2XLS(ResultM, 'ResultMean.xlsx',param)
  716. save('results.mat', 'ResultM')
  717. end
  719. toc

ASL_main.m at commit 3c137e3, under MIT · at the source

Overview

Authors: Vadim Malis1, Hye Na Jung1,2, Yoshiki Kuwatsuru1,3, Won C Bae1, Mitsue Miyazaki1
ORCID iDs: Vadim Malis, Won C Bae, Mitsue Miyazaki
  1. Department of Radiology, University of California San Diego, La Jolla, CA, USA
  2. Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea
  3. Department of Radiology, Juntendo University, Tokyo, Japan
Institutions: University of California San Diego (United States); Korea University Guro Hospital (South Korea); Juntendo University (Japan)
Journal: Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, volume 46, issue 9, article 0271678X261455452
Dates: received 21 January 2026; accepted 16 May 2026; published online 1 June 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1177/0271678x261455452 · PMID 42219987 · PMCID PMC13272186 · OpenAlex W7163058769
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics, fMRI & imaging
Keywords: Aging, cerebrospinal fluid, meningeal transport, neurofluid physiology, Time-SLIP MRI
MeSH: Aging*, Cerebrospinal Fluid*, Dura Mater*, Meninges*, Adult, Aged, Aged, 80 and over, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Canon Medical Systems (35938); NIA NIH HHS (R01 AG087407, R01 AG076692); National Institutes of Health (R01AG076692 and R01AG087407)
Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

Cerebrospinal fluid (CSF) transport along meningeal pathways contributes to brain fluid homeostasis and is thought to change with aging, yet quantitative, region-resolved measurements across lifespan remain limited. We hypothesized that meningeal CSF transport dynamics exhibit region-specific age associations rather than uniform decline. We applied non-contrast time-spatial labeling inversion pulse (Time-SLIP) MRI combined with a bi-component analytical model to characterize CSF transport dynamics in a healthy, activity-controlled cohort of 64 adults aged 19–86 years. Signal increase ratio time courses were decomposed into a fast Gaussian bulk-displacement and a slower Γ-variate perfusion-like component. Linear mixed-effects models were used to assess regional and age-related effects. Model fits were robust across all participants (R2 = 0.97). CSF transport metrics differed significantly across parasagittal dura subregions and the superior sagittal sinus. Age associations were heterogeneous: the perfusion-like component declined with age in upper parasagittal dura, increased in lower parasagittal regions, and remained relatively stable within the superior sagittal sinus. Additional timing and amplitude parameters exhibited region-specific age trajectories. These findings demonstrate regionally heterogeneous aging effects on meningeal CSF transport dynamics and suggest age-related redistribution rather than uniform decline, providing a noninvasive framework for assessing physiological remodeling of CSF transport with aging.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

vmalis/TimeSLIP-CSF-transport

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3c137e3f5ba114855c1a7bbee1ec12c2bd81249e, 21 January 2026
Languages: MATLAB (67), Java (1)
Size: 119 files, 68 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, documentation
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: Image Processing Toolbox (11 files), export_fig (5 files), Curve Fitting Toolbox (5 files), Optimization Toolbox (2 files), Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
70 files
At the source: github.com/vmalis/TimeSLIP-CSF-transport

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 68 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 and code availability

All analysis code used in this study is publicly available at GitHub (https://github.com/vmalis/TimeSLIP-CSF-transport). An anonymized Time-SLIP MRI test dataset supporting the findings of this work is available via Zenodo (https://doi.org/10.5281/zenodo.17982328).

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 13 MeSH terms, 3 funders, 34 references.

Cite

This paper

Malis, V., Jung, H. N., Kuwatsuru, Y., Bae, W. C., & Miyazaki, M. (2026). Meningeal CSF transport varies across parasagittal dura subregions with age in humans. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 46(9), 0271678X261455452. https://doi.org/10.1177/0271678x261455452

BibTeX

@article{malis2026meningeal,
author = {Malis, Vadim and Jung, Hye Na and Kuwatsuru, Yoshiki and Bae, Won C and Miyazaki, Mitsue},
title = {{Meningeal CSF transport varies across parasagittal dura subregions with age in humans}},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
year = {2026},
month = jun,
volume = {46},
number = {9},
pages = {0271678X261455452},
publisher = {SAGE Publications},
issn = {0271-678X},
doi = {10.1177/0271678x261455452},
url = {https://doi.org/10.1177/0271678x261455452},
pmid = {42219987},
pmcid = {PMC13272186}
}

RIS

TY - JOUR
AU - Malis, Vadim
AU - Jung, Hye Na
AU - Kuwatsuru, Yoshiki
AU - Bae, Won C
AU - Miyazaki, Mitsue
TI - Meningeal CSF transport varies across parasagittal dura subregions with age in humans
T2 - Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
J2 - J Cereb Blood Flow Metab
PY - 2026
DA - 2026/06/01
VL - 46
IS - 9
SP - 0271678X261455452
SN - 0271-678X
PB - SAGE Publications
DO - 10.1177/0271678x261455452
UR - https://doi.org/10.1177/0271678x261455452
LA - en
ER -

CSL-JSON

{
"id": "10.1177/0271678x261455452",
"type": "article-journal",
"title": "Meningeal CSF transport varies across parasagittal dura subregions with age in humans",
"container-title": "Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism",
"author": [
{
"family": "Malis",
"given": "Vadim"
},
{
"family": "Jung",
"given": "Hye Na"
},
{
"family": "Kuwatsuru",
"given": "Yoshiki"
},
{
"family": "Bae",
"given": "Won C"
},
{
"family": "Miyazaki",
"given": "Mitsue"
}
],
"container-title-short": "J Cereb Blood Flow Metab",
"volume": "46",
"issue": "9",
"page": "0271678X261455452",
"DOI": "10.1177/0271678x261455452",
"PMID": "42219987",
"PMCID": "PMC13272186",
"ISSN": "0271-678X",
"publisher": "SAGE Publications",
"URL": "https://doi.org/10.1177/0271678x261455452",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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