Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort.

Code ↔ Paper

21 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 21 matches

[1] § Materials and methods › Statistical analysis ↔ Statistical_analysis_3T.m, lines 1–34 · score 0.73 · Holm Bonferroni correction, confidence intervals, linear regression, field strength, CEST amplitude, coefficients
[2] § Materials and methods › Statistical analysis ↔ Statistical_analysis_7T.m, lines 1–35 · score 0.73 · Holm Bonferroni correction, confidence intervals, linear regression, field strength, CEST amplitude, coefficients
[3] § Materials and methods › Statistical analysis ↔ Plot_Result_3T.m, lines 502–527 · score 0.73 · quantifies intra, CEST signals, tissue mask, std, variation, CoVs
[4] § Materials and methods › Statistical analysis ↔ Plot_Result_7T.m, lines 599–628 · score 0.73 · quantifies intra, CEST signals, tissue mask, std, variation, CoVs
[5] § Materials and methods ↔ Plot_Result_7T.m, lines 1–35 · score 0.70 · chemical exchange saturation, glioma patients, Lorentzian fitting, MRI, transfer, pipeline
[6] § Materials and methods › Post-processing › Anatomical segmentation ↔ Plot_Result_3T.m, lines 262–294 · score 0.65 · Segmentation masks, deep grey matter, atlas, hippocampus, SPM, DL
[7] § Materials and methods › Post-processing › Anatomical segmentation ↔ Plot_Result_7T.m, lines 326–362 · score 0.64 · Segmentation masks, deep grey matter, atlas, hippocampus, SPM, DL
[8] § Materials and methods › Statistical analysis ↔ comparison_tumour_3T_7T.m, lines 39–178 · score 0.62 · IDH mutation status, contralateral healthy tissue, mask, tumor, tumours, patients
[9] § Materials and methods ↔ Plot_Result_3T.m, lines 1–31 · score 0.61 · chemical exchange saturation, glioma patients, MRI, transfer, Segmentation, deep
[10] § Materials and methods › 3T acquisition protocol ↔ Plot_Result_7T.m, lines 436–531 · score 0.58 · B1 mapping, tissue segmentation, MPRAGE, space, FLAIR, ppm
[11] § Materials and methods › 7T acquisition protocol ↔ Plot_Result_7T.m, lines 436–531 · score 0.58 · anatomical reference, B1 mapping, MPRAGE, space, FLAIR, ppm
[12] § Results › Age dependence of CEST pools ↔ Statistical_analysis_3T.m, lines 1–34 · score 0.58 · Holm Bonferroni correction, confidence intervals, Linear regressions, CEST amplitude, CEST pool, Age
[13] § Materials and methods › 3T acquisition protocol ↔ Plot_Result_3T.m, lines 372–457 · score 0.56 · B1 mapping, tissue segmentation, space, FLAIR, ppm, B0
[14] § Materials and methods › Post-processing › Quality control and tissue mask definition ↔ Plot_Result_3T.m, lines 297–335 · score 0.56 · uncertainty map, tissue masks, threshold, Quality, GM, WM
[15] § Materials and methods › Post-processing › Quality control and tissue mask definition ↔ Plot_Result_7T.m, lines 365–403 · score 0.56 · uncertainty map, tissue masks, threshold, Quality, GM, WM
[16] § Results › IDH mutation and tumour grading ↔ comparison_tumour_3T_7T.m, lines 1–36 · score 0.55 · IDH mutation status, Lorentzian fitting, CEST amplitudes, CEST pool, grade, tumour
[17] § Results ↔ Plot_Result_7T.m, lines 884–965 · score 0.53 · direct saturation, pool Lorentzian, component, DS, rNOE, spectra
[18] § Results › Age dependence of CEST pools ↔ Statistical_analysis_3T.m, lines 102–141 · score 0.53 · confidence intervals, Linear regressions, diamonds, rNOE, female, CEST pool
[19] § Results ↔ Plot_Result_3T.m, lines 372–457 · score 0.52 · B1 field maps, anatomical images, B0, segmentation, amplitudes, patients
[20] § Materials and methods › Statistical analysis ↔ comparison_tumour_3T_7T.m, lines 471–518 · score 0.52 · Wilcoxon rank sum, tumour
[21] § Results › IDH mutation and tumour grading ↔ comparison_tumour_3T_7T.m, lines 39–178 · score 0.50 · IDH mutation status, Boxplots, rNOE, wildtype, contralateral, CEST pool

Paper

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The authors' code

MATLAB · 1,144 lines · 49 KB · MIT · 7 matches

% =========================================================================
% Plot_Result_7T.m
%
% Description:
% This script processes and visualizes 7T CEST (Chemical Exchange
% Saturation Transfer) MRI data for a cohort of glioma patients.
% Specifically, it:
% 1) Loads per-subject CEST acquisition data and anatomical images.
% 2) Displays tissue segmentation overlays and fitted CEST pool maps.
% 3) Extracts region-of-interest (ROI) metrics for white matter (WM),
% grey matter (GM), deep GM, hippocampus, and tumor tissue.
% 4) Computes the coefficient of variation (CoV) per tissue and pool.
% 5) Exports summary tables for downstream statistical analysis.
%
% Two analysis pipelines are supported, selectable via the 'picae' flag:
% - Lorentzian fitting (default, picae = 0)
% - PICAE deep-learning reconstruction (picae = 1)
%
% Reference manuscript:
% "Field Strength-Dependent Sensitivity of Chemical Exchange Saturation
% Transfer: A Methodological Comparison of 3T and 7T in a Clinical
% Cohort."
%
% Author: Milena Capiglioni, University of Bern, 2025
%
% Notes:
% - Auxiliary functions are expected in a subfolder named 'aux' located
% in the script directory.
% - For patient confidentiality, only co-registered low-resolution
% anatomical images are shared publicly.
% =========================================================================
clear global; clc; clear; close all;
restoredefaultpath;
%% Setup
% -------------------------------------------------------------------------
% Define the script directory and add auxiliary functions to the path.
% Update 'script_dir' to match your local installation.
% -------------------------------------------------------------------------
cd(fileparts(matlab.desktop.editor.getActiveFilename));
script_dir = fileparts(matlab.desktop.editor.getActiveFilename);
addpath(genpath(fullfile(script_dir,'aux_scripts')))
% Toggle to enable/disable saving of figures and output tables.
% Set to 1 to save, 0 to skip saving.
save_stuff = 1;
%% Patient Selection and Path Configuration
% -------------------------------------------------------------------------
% Define data paths and identify patient folders.
% Patient folders are expected to follow the naming convention 'p*'
% (e.g., 'p01', 'p02', ...).
% -------------------------------------------------------------------------
% Root directory containing all 7T patient subdirectories
data_path = '/path/to/folder/where/data/is/';
work_dir_aux_7T = [data_path 'cest_eval_7T' filesep];
cd(work_dir_aux_7T);
files = dir(work_dir_aux_7T);
dirFlag = startsWith({files.name},{'p'})&[files.isdir];
patient_folder = files(dirFlag);
% Load patient metadata table (contains age, sex, WHO grade, IDH status)
load([data_path 'patient_table_share.mat'])
% Segmentation method selector.
% Options:
% 'spm' - SPM-based tissue probability maps
% 'DL' - Deep Learning segmentation with DiReCT co-registration
% Note: Only co-registered low-resolution anatomical images are shared
% publicly due to patient confidentiality constraints.
seg_method = 'DL';
% Flag to optionally compute and display MTR-Rex contrast maps.
% Note: MTR-Rex requires the CEST toolbox from the FAU group.
% Set to 1 to enable.
MTR_analysis = 0;
% Analysis pipeline selector.
% 0 - Lorentzian fitting (standard pipeline)
% 1 - PICAE deep-learning reconstruction
% Note: MTR-Rex is not currently supported for PICAE output.
picae = 0;
if ~picae
out_folder = [data_path 'cest_eval_7T/Output_CEST_paper/'];
else
out_folder = [data_path 'cest_eval_7T/Output_CEST_picae_paper/'];
MTR_analysis = 0; % MTR-Rex generation from PICAE maps is not yet implemented
end
if save_stuff && ~exist(out_folder,'dir')
mkdir(out_folder);
end
%% Per-Subject Loop: Anatomical and CEST Map Visualization
% -------------------------------------------------------------------------
% For each patient, this loop:
% - Loads CEST spectral fit results (Lorentzian or PICAE) and anatomical
% images.
% - Identifies the axial slice with maximum tumor extent.
% - Generates overlay figures showing segmentation and CEST maps.
% - Extracts mean CEST pool amplitudes per tissue ROI.
% -------------------------------------------------------------------------
RF_table = table();
for ii = 1:size(patient_folder,1)
% Build paths to the patient-specific CEST data and segmentation output
work_dir_CEST = [work_dir_aux_7T patient_folder(ii).name filesep];
output_folder = [work_dir_CEST 'results' filesep seg_method '_seg' filesep];
% Verify that the segmentation output folder exists before proceeding
if ~exist(output_folder,'dir')
error('Segmentation folder missing, revise your paths!')
end
%% Load CEST Reference Image
% ---------------------------------------------------------------------
% The reference image is acquired at 720 nT saturation amplitude.
% Two possible naming conventions are checked to accommodate different
% acquisition protocols.
% ---------------------------------------------------------------------
cest_ref = getPathToFileContainingString([work_dir_CEST 'nii' filesep],'720nT_REG','nii');
if isempty(cest_ref)
cest_ref = getPathToFileContainingString([work_dir_CEST 'nii' filesep],'720nT_1Tx_RO_REG','nii');
end
%% Load Fitted CEST Parameters (Lorentzian or PICAE)
% ---------------------------------------------------------------------
% For the Lorentzian pipeline, the B1-corrected fitted Z-spectrum file
% ('Zfitted*B1_*') is located in the Zeval_001 subdirectory, excluding
% any backup files. For PICAE, the output nifti file is located in the
% Zeval_niftis_picae directory.
% ---------------------------------------------------------------------
if ~picae
Zeval_dir = [work_dir_CEST 'Zeval' filesep 'Zeval_001' filesep];
filename = dir(Zeval_dir);
myindices = ~cellfun(@isempty,regexp({filename.name},'Zfitted\w*B1_','match')) & ~contains({filename.name}, "bkup");
else
Zeval_dir = [work_dir_CEST 'Zeval_niftis_picae' filesep];
filename = dir(Zeval_dir);
myindices = ~cellfun(@isempty,regexp({filename.name},'out_PICAE','match'));
end
Zfit_file = filename(myindices);
% Attempt to load the fitted parameters and the reference CEST image.
% If either file is missing, record NaN for all metrics and skip this
% subject.
try
load([Zeval_dir Zfit_file.name]) % Loading may take a while for large files
cest_im = load_nii(cest_ref);
cest_im = cest_im.img;
catch
% Mark all ROI metrics as missing for this subject
age(ii) = nan;
amide_wm(ii) = nan;
amine_wm(ii) = nan;
noe_wm(ii) = nan;
MT_wm(ii) = nan;
amide_gm(ii) = nan;
amine_gm(ii) = nan;
noe_gm(ii) = nan;
MT_gm(ii) = nan;
amide_tumor(ii) = nan;
amine_tumor(ii) = nan;
noe_tumor(ii) = nan;
MT_tumor(ii) = nan;
amide_hip(ii) = nan;
amine_hip(ii) = nan;
noe_hip(ii) = nan;
MT_hip(ii) = nan;
amide_deep_gm(ii) = nan;
amine_deep_gm(ii) = nan;
noe_deep_gm(ii) = nan;
MT_deep_gm(ii) = nan;
amide_global(ii) = nan;
amine_global(ii) = nan;
noe_global(ii) = nan;
MT_global(ii) = nan;
continue;
end
%% Identify Pool Indices and Load Field Maps
% ---------------------------------------------------------------------
% For Lorentzian fitting: pool indices are read from P.FIT.PoolNames.
% Note: The ordering of NOE and MT in P.FIT.PoolNames was verified
% against FAU_7T_1Tx_CEST_Evaluation_IMPI04_MC.m. The commented-out
% layout below shows the expected structure for reference.
%
% For PICAE: pool indices are fixed by convention from the PICAE Python
% output format (verified against the PICAE source code).
%
% In both cases, the five Lorentzian pool parameters are:
% Index 1: Zi (baseline offset)
% Indices 2-4: Water (Amplitude, width, center position)
% Indices 5-7: Amide/APT (Amplitude, width, center position)
% Indices 8-10: MT (Amplitude, width, center position)
% Indices 11-13: NOE (Amplitude, width, center position)
% Indices 14-16: Amine (Amplitude, width, center position)
% ---------------------------------------------------------------------
if ~picae
% Commented reference layout for P.FIT (retained for documentation):
% P.FIT.PoolNames = {'MISC','Water','Water','Water','Amide','Amide','Amide','MT','MT','MT','NOE','NOE','NOE','Amine','Amine','Amine'};
% P.FIT.ParaNames = {'Offset','A','width','w_c','A','width','w_c','A','width','w_c','A','width','w_c','A','width','w_c'};
Amide_pos = find(strcmp(P.FIT.PoolNames, 'Amide')==1); Amide_pos = Amide_pos(1);
Amine_pos = find(strcmp(P.FIT.PoolNames, 'Amine')==1); Amine_pos = Amine_pos(1);
NOE_pos = find(strcmp(P.FIT.PoolNames, 'NOE')==1); NOE_pos = NOE_pos(1);
MT_pos = find(strcmp(P.FIT.PoolNames, 'MT')==1); MT_pos = MT_pos(1);
% Load B0 and B1 field maps from the WASABI-derived file
myindices = ~cellfun(@isempty,regexp({filename.name},'B1_relative','match'));
B1B0_file = filename(myindices);
B0B1 = load([Zeval_dir B1B0_file.name]);
B0_map = B0B1.dB0_stack_int;
B1_map = B0B1.B1map;
else
% PICAE pool index convention (from PICAE Python source):
% 1 = Amide, 2 = NOE, 3 = MT, 4 = Amine
popt = ampls_p72_4d; % Use uncorrected maps: Zeval_PICAE.ampls_uncorr_4d
Amide_pos = 1;
Amine_pos = 4;
NOE_pos = 2;
MT_pos = 3;
% Load B0 and B1 maps from the Lorentzian Zeval directory.
% The brain mask from the Lorentzian fit is used to NaN out
% non-brain voxels in the PICAE output.
Zeval_dir = [work_dir_CEST 'Zeval' filesep 'Zeval_001' filesep];
filename = dir(Zeval_dir);
myindices = ~cellfun(@isempty,regexp({filename.name},'B1_relative','match'));
B1B0_file = filename(myindices);
B0B1 = load([Zeval_dir B1B0_file.name]);
B0_map_Zeval = B0B1.dB0_stack_int;
B0_map = pos_4d(:,:,:,end);
B0_map(isnan(B0_map_Zeval)) = nan;
B1_map = b1_4d;
uncertainty_picae = uncert_4d; % Full-fit uncertainty from PICAE
end
%% Load FLAIR Image
% ---------------------------------------------------------------------
% Load the co-registered FLAIR image for anatomical reference.
% If absent, fall back to the MPRAGE T1-weighted image.
% ---------------------------------------------------------------------
anat = 'flair';
flair_cor_path = getPathToFileContainingString(output_folder,'rflair','.nii');
if ~exist(flair_cor_path,'file')
disp('Corregistered flair missing, check your paths')
flair_im = zeros(size(popt(:,:,:,1)));
anat = 'mprage';
else
flair_im = load_untouch_nii(flair_cor_path);
flair_im = flair_im.img;
end
%% Load T1-Weighted Image and Tissue Segmentation Masks
% ---------------------------------------------------------------------
% Depending on the chosen segmentation method, load the appropriate
% co-registered T1-weighted image and tissue class masks.
% ---------------------------------------------------------------------
if strcmp(seg_method,'spm')
% Locate the T1 folder within the output directory.
% Two common naming conventions are checked; if neither matches,
% the user is prompted to select the folder manually.
foldernames = dir(output_folder);
dirFlag = strcmp({foldernames.name},'t1_mprage_sag_p2_0.6mm_ns')&[foldernames.isdir];
if ~any(dirFlag)
dirFlag = strcmp({foldernames.name},'t1_cs_mp2rage_sag_0.6mm')&[foldernames.isdir];
if ~any(dirFlag)
aux_path = uigetdir(work_dir_anat,'Select T1 folder');
aux = strsplit(aux_path,filesep);
dirFlag = strcmp({foldernames.name},aux{end-1})&[foldernames.isdir];
end
end
% Build paths to the co-registered T1 image and SPM tissue segments
T1_cor_path = getPathToFileContainingString(output_folder,['r' foldernames(dirFlag).name] ,'.nii');
if ~exist(T1_cor_path,'file') && ~exist(T1_seg_cor_path{1},'file')
T1_cor_path = getPathToFileContainingString(output_folder,'t1_cs_mp2rage_sag_0.6mm','.nii');
end
for s = 1:3
T1_seg_cor_path{s} = getPathToFileContainingString(output_folder,['rc' num2str(s) foldernames(dirFlag).name],'.nii');
end
else
% For DL segmentation: check whether co-registered files exist
T1_cor_path = getPathToFileContainingString(output_folder,'rT1w_norm.nii');
T1_seg_cor_path{1} = getPathToFileContainingString(output_folder,'rT1w_norm_seg.nii');
if ~exist(T1_cor_path,'file') && ~exist(T1_seg_cor_path{1},'file')
disp('Corregistered T1-w missing, check your paths')
end
end
T1_im = load_untouch_nii(T1_cor_path);
T1_im = T1_im.img;
% Verify that tumor segmentation files are present
tumor_seg{1} = [output_folder 'rtumor_SCAN2020_class.nii'];
tumor_seg{2} = [output_folder 'rtumor_SCAN2020_unc_whole.nii'];
if ~exist(tumor_seg{1},'file') || ~exist(tumor_seg{2},'file')
disp('Tumor masks missing, check your paths')
end
%% Construct Tissue Masks from Segmentation
% ---------------------------------------------------------------------
% Build binary masks for each tissue class. For SPM, probabilistic
% maps are loaded directly. For DL segmentation, masks are derived
% from a labeled atlas image using standard FreeSurfer label indices.
% ---------------------------------------------------------------------
if strcmp(seg_method,'spm')
gm_mask = load_nii(T1_seg_cor_path{1});
gm_mask = gm_mask.img;
wm_mask = load_nii(T1_seg_cor_path{2});
wm_mask = wm_mask.img;
csf_mask = load_nii(T1_seg_cor_path{3});
csf_mask = csf_mask.img;
deep_gm_mask = uint8(zeros(size(csf_mask)));
else
seg_mask = load_nii(T1_seg_cor_path{1});
% White matter: labels 2 (left WM) and 41 (right WM)
wm_mask = (seg_mask.img == 2) + (seg_mask.img == 41);
% Cortical grey matter: labels 1001-1035 (left) and 2001-2035 (right)
gm_mask = zeros(size(seg_mask.img));
for i = 1:35
gm_mask = gm_mask + (seg_mask.img == 1000+i) + (seg_mask.img == 2000+i) ;
end
% Hippocampus: labels 17 (left) and 53 (right)
hip_mask = (seg_mask.img == 17) + (seg_mask.img == 53);
% Deep grey matter structures (includes hippocampus).
% Label list covers subcortical nuclei as defined by FreeSurfer.
list_gm = [10:13 16:28 49:60];
deep_gm_mask = double(ismember(seg_mask.img, list_gm));
end
% Combined brain mask (used for tumor volume normalization)
brain_mask = wm_mask + gm_mask + deep_gm_mask;
%% Construct and Refine the Tumor Mask
% ---------------------------------------------------------------------
% The primary tumor mask is derived from the SCAN2020 segmentation.
% An uncertainty map is thresholded and added to expand the exclusion
% zone, ensuring that uncertain tumor voxels are also removed from
% healthy tissue masks.
%
% The hippocampus is explicitly excluded from the tumor mask, as the
% segmentation algorithm can misclassify it as tumor tissue due to
% T2 hyperintensities (validated visually in collaboration with a
% radiologist).
%
% Note: This conservative expansion may exclude some healthy brain
% tissue near the tumor boundary, particularly the hippocampus.
% ---------------------------------------------------------------------
tumor_mask = load_nii(tumor_seg{1});
tumor_mask = tumor_mask.img;
tumor_mask_certain = tumor_mask;
tumor_mask(tumor_mask ~= 0) = 1;
% Expand the tumor mask using the uncertainty map.
% Voxels with uncertainty above the threshold are treated as tumor.
threhsold = 10;
tumor_unc = load_nii(tumor_seg{2});
tumor_unc = tumor_unc.img;
tumor_unc(tumor_unc <= threhsold) = 0;
tumor_unc(tumor_unc > threhsold) = 1;
tumor_mask = tumor_mask + tumor_unc;
% Remove hippocampal voxels from the tumor mask to prevent
% misclassification
tumor_mask(hip_mask == 1) = 0;
tumor_mask = double(tumor_mask);
% Compute tumor volume as a percentage of total brain volume
% (WM + cortical GM + deep GM) for quality control
global_size = nnz(wm_mask + gm_mask + deep_gm_mask);
tumor_size = nnz(tumor_mask);
tumor_per(ii) = tumor_size*100/global_size;
%% Identify the Axial Slice with Maximum Tumor Extent
% ---------------------------------------------------------------------
% The tumor slice is the axial slice containing the greatest number
% of non-zero tumor voxels. Edge slices (1, 2, 17, 18) are remapped
% to the nearest valid interior slice to avoid boundary artefacts.
% ---------------------------------------------------------------------
max_nnz = 0;
for s = 1:size(tumor_mask,3)
nnz_values = nnz(tumor_mask(:,:,s));
if nnz_values > max_nnz
max_nnz = nnz_values;
tumor_slice = s;
end
end
% Remap boundary slices to avoid display artefacts
if tumor_slice == 18 || tumor_slice == 17
tumor_slice = 16;
elseif tumor_slice == 1 || tumor_slice == 2
tumor_slice = 3;
end
% Exclude tumor voxels from all healthy tissue masks to prevent
% contamination of healthy-tissue ROI statistics
gm_mask(tumor_mask ~= 0) = 0;
wm_mask(tumor_mask ~= 0) = 0;
csf_mask(tumor_mask ~= 0) = 0;
deep_gm_mask(tumor_mask ~= 0) = 0;
%% Figure 3: Anatomical and CEST Map Overview
% ---------------------------------------------------------------------
% Generates an 8-panel overview figure for each subject showing:
% Panel 1: FLAIR (or MPRAGE if FLAIR is unavailable) at tumor slice
% Panel 2: CEST reference image with tissue segmentation overlays
% (GM = green, WM = blue, deep GM = yellow, tumor = red)
% Panel 3: B0 field map with histogram inset
% Panel 4: B1 field map with histogram inset
% Panels 5-8: CEST pool amplitude maps (amide, rNOE, MT, amine)
% ---------------------------------------------------------------------
ff = figure(3);
t = tiledlayout(2,4,'TileSpacing','compact','Padding','compact');
txt = [ patient_folder(ii).name ' - age ' num2str(patient_table.age(ii)) ' - slice ' num2str(tumor_slice) ' - tumor ' num2str(tumor_per(ii),'%0.2f') ' %'];
if picae
txt = [txt ' - picae'];
end
sgtitle(txt,'interpreter','none');
% Panel 1: Anatomical reference image (FLAIR preferred, MPRAGE fallback)
ax = nexttile(t);
if strcmp(anat,'flair')
imshow(imrotate(flair_im(:,:,tumor_slice),90),[]);
title('FLAIR tumor slice')
else
imshow(imrotate(T1_im(:,:,tumor_slice),90),[]);
title('MPRAGE tumor slice')
end
% Panel 2: CEST reference image with color-coded tissue segmentation
ax2 = nexttile(t);
imshow(imrotate(cest_im(:,:,tumor_slice),90),[]);
% Overlay: cortical GM (green, semi-transparent)
gm_mask_t = imrotate(gm_mask(:,:,tumor_slice),90);
green = cat(3, zeros(size(gm_mask_t)), ones(size(gm_mask_t)), zeros(size(gm_mask_t)));
hold on;
auxim = imshow(green,'Parent',ax2);
hold off;
set(auxim,'AlphaData', 0.2.*double(gm_mask_t./max(gm_mask_t,[],'all')))
% Overlay: white matter (blue, semi-transparent)
wm_mask_t = imrotate(wm_mask(:,:,tumor_slice),90);
blue = cat(3, zeros(size(wm_mask_t)), zeros(size(wm_mask_t)), ones(size(wm_mask_t)));
hold on;
auxim2 = imshow(blue,'Parent',ax2);
hold off;
set(auxim2,'AlphaData', 0.2.*double(wm_mask_t./max(wm_mask_t,[],'all')))
% Overlay: deep GM structures (yellow, semi-transparent)
dgm_mask_t = imrotate(deep_gm_mask(:,:,tumor_slice),90);
yellow = cat(3, ones(size(dgm_mask_t)), ones(size(dgm_mask_t)), zeros(size(dgm_mask_t)));
hold on;
auxim3 = imshow(yellow,'Parent',ax2);
hold off;
set(auxim3,'AlphaData', 0.2.*double(dgm_mask_t./max(dgm_mask_t,[],'all')))
% Overlay: tumor region (red, semi-transparent)
tumor_mask_t = imrotate(tumor_mask(:,:,tumor_slice),90);
red = cat(3, ones(size(tumor_mask_t)), zeros(size(tumor_mask_t)), zeros(size(tumor_mask_t)));
hold on;
auxim4 = imshow(red,'Parent',ax2);
hold off;
set(auxim4,'AlphaData', 0.2.*double(tumor_mask_t./max(tumor_mask_t,[],'all')))
title('Segmented CEST')
% Panel 3: B0 field map (ppm), with quality control percentage
ax3 = nexttile(t);
try
imshow(imrotate(B0_map(:,:,tumor_slice),90),[]);
caxis([-0.4 0.4])
colorbar();
title('B0 Map (ppm)')
% Fraction of voxels exceeding the B0 acceptability threshold (±0.4 ppm)
b0_total = nnz(~isnan(B0_map));
B0_per(ii) = (sum(B0_map > 0.4 | B0_map < -0.4,'all')/b0_total)*100;
catch
title('No B0 in B1_relative.mat')
end
% Panel 4: B1 field map (relative units), with quality control percentage
ax4 = nexttile(t);
try
B1_map(isnan(B0_map)) = nan;
imshow(imrotate(B1_map(:,:,tumor_slice),90),[]);
caxis([0.4 1.6])
colorbar();
title('B1 map (rel)')
% Fraction of voxels outside the B1 acceptability range [0.5, 1.5]
b1_total = nnz(~isnan(B1_map));
B1_per(ii) = (sum(B1_map < 0.5 | B1_map > 1.5,'all')/b1_total)*100;
catch
title('No B1 in B1_relative.mat')
end
%% Extract CEST Pool Amplitude Maps
% ---------------------------------------------------------------------
% Squeeze the fitted parameter array (popt) into 3D maps for each pool.
% For PICAE outputs, non-brain voxels are explicitly set to NaN using
% the brain mask derived from the Lorentzian Zeval output.
% ---------------------------------------------------------------------
amide_map = squeeze(popt(:,:,:,Amide_pos));
amine_map = squeeze(popt(:,:,:,Amine_pos));
NOE_map = squeeze(popt(:,:,:,NOE_pos));
MT_map = squeeze(popt(:,:,:,MT_pos));
if picae
amide_map(isnan(B0_map_Zeval)) = nan;
amine_map(isnan(B0_map_Zeval)) = nan;
NOE_map(isnan(B0_map_Zeval)) = nan;
MT_map(isnan(B0_map_Zeval)) = nan;
end
%% Compute Mean CEST Amplitudes per Tissue ROI
% ---------------------------------------------------------------------
% For each CEST pool (amide/APT, amine, rNOE, MT), compute the mean
% amplitude within each tissue mask across the full 3D volume.
% ---------------------------------------------------------------------
% White matter
amide_wm(ii) = mean(amide_map(logical(wm_mask)),'all','omitnan');
amine_wm(ii) = mean(amine_map(logical(wm_mask)),'all','omitnan');
noe_wm(ii) = mean(NOE_map(logical(wm_mask)),'all','omitnan');
MT_wm(ii) = mean(MT_map(logical(wm_mask)),'all','omitnan');
% Cortical grey matter
amide_gm(ii) = mean(amide_map(logical(gm_mask)),'all','omitnan');
amine_gm(ii) = mean(amine_map(logical(gm_mask)),'all','omitnan');
noe_gm(ii) = mean(NOE_map(logical(gm_mask)),'all','omitnan');
MT_gm(ii) = mean(MT_map(logical(gm_mask)),'all','omitnan');
% Tumor region
amide_tumor(ii) = mean(amide_map(logical(tumor_mask)),'all','omitnan');
amine_tumor(ii) = mean(amine_map(logical(tumor_mask)),'all','omitnan');
noe_tumor(ii) = mean(NOE_map(logical(tumor_mask)),'all','omitnan');
MT_tumor(ii) = mean(MT_map(logical(tumor_mask)),'all','omitnan');
% Store full voxel-level distributions within the tumor for later analysis
pat{ii}.amide_tumor = amide_map(logical(tumor_mask));
pat{ii}.amine_tumor = amine_map(logical(tumor_mask));
pat{ii}.noe_tumor = NOE_map(logical(tumor_mask));
pat{ii}.MT_tumor = MT_map(logical(tumor_mask));
% Hippocampus
amide_hip(ii) = mean(amide_map(logical(hip_mask)),'all','omitnan');
amine_hip(ii) = mean(amide_map(logical(hip_mask)),'all','omitnan');
noe_hip(ii) = mean(NOE_map(logical(hip_mask)),'all','omitnan');
MT_hip(ii) = mean(MT_map(logical(hip_mask)),'all','omitnan');
% Deep grey matter structures
amide_deep_gm(ii) = mean(amide_map(logical(deep_gm_mask)),'all','omitnan');
amine_deep_gm(ii) = mean(amine_map(logical(deep_gm_mask)),'all','omitnan');
noe_deep_gm(ii) = mean(NOE_map(logical(deep_gm_mask)),'all','omitnan');
MT_deep_gm(ii) = mean(MT_map(logical(deep_gm_mask)),'all','omitnan');
% Record number of non-zero deep GM voxels for quality control
ndgm(ii) = nnz(deep_gm_mask);
%% Compute Subject-Level Coefficient of Variation (CoV)
% ---------------------------------------------------------------------
% CoV is computed as 100 * (std / mean) within each tissue mask for
% each CEST pool. This quantifies intra-tissue spatial variability
% of the CEST signal on a per-subject basis.
% ---------------------------------------------------------------------
% White matter CoV
cov_ind.amide_wm(ii) = 100*mean(std(amide_map(logical(wm_mask)),[],'all','omitnan')/mean(amide_map(logical(wm_mask)),'all','omitnan'));
cov_ind.amine_wm(ii) = 100*std(amine_map(logical(wm_mask)),[],'all','omitnan')/mean(amine_map(logical(wm_mask)),'all','omitnan');
cov_ind.noe_wm(ii) = 100*std(NOE_map(logical(wm_mask)),[],'all','omitnan')/mean(NOE_map(logical(wm_mask)),'all','omitnan');
cov_ind.MT_wm(ii) = 100*std(MT_map(logical(wm_mask)),[],'all','omitnan')/mean(MT_map(logical(wm_mask)),'all','omitnan');
% Cortical grey matter CoV
cov_ind.amide_gm(ii) = 100*std(amide_map(logical(gm_mask)),[],'all','omitnan')/mean(amide_map(logical(gm_mask)),'all','omitnan');
cov_ind.amine_gm(ii) = 100*std(amine_map(logical(gm_mask)),[],'all','omitnan')/mean(amine_map(logical(gm_mask)),'all','omitnan');
cov_ind.noe_gm(ii) = 100*std(NOE_map(logical(gm_mask)),[],'all','omitnan')/mean(NOE_map(logical(gm_mask)),'all','omitnan');
cov_ind.MT_gm(ii) = 100*std(MT_map(logical(gm_mask)),[],'all','omitnan')/mean(MT_map(logical(gm_mask)),'all','omitnan');
% Tumor CoV
cov_ind.amide_tumor(ii) = 100*std(amide_map(logical(tumor_mask)),[],'all','omitnan')/mean(amide_map(logical(tumor_mask)),'all','omitnan');
cov_ind.amine_tumor(ii) = 100*std(amine_map(logical(tumor_mask)),[],'all','omitnan')/mean(amine_map(logical(tumor_mask)),'all','omitnan');
cov_ind.noe_tumor(ii) = 100*std(NOE_map(logical(tumor_mask)),[],'all','omitnan')/mean(NOE_map(logical(tumor_mask)),'all','omitnan');
cov_ind.MT_tumor(ii) = 100*std(MT_map(logical(tumor_mask)),[],'all','omitnan')/mean(MT_map(logical(tumor_mask)),'all','omitnan');
% Deep grey matter CoV
cov_ind.amide_deep_gm(ii) = 100*std(amide_map(logical(deep_gm_mask)),[],'all','omitnan')/mean(amide_map(logical(deep_gm_mask)),'all','omitnan');
cov_ind.amine_deep_gm(ii) = 100*std(amine_map(logical(deep_gm_mask)),[],'all','omitnan')/mean(amine_map(logical(deep_gm_mask)),'all','omitnan');
cov_ind.noe_deep_gm(ii) = 100*std(NOE_map(logical(deep_gm_mask)),[],'all','omitnan')/mean(NOE_map(logical(deep_gm_mask)),'all','omitnan');
cov_ind.MT_deep_gm(ii) = 100*std(MT_map(logical(deep_gm_mask)),[],'all','omitnan')/mean(MT_map(logical(deep_gm_mask)),'all','omitnan');
%% Panels 5-8: CEST Pool Amplitude Maps
% ---------------------------------------------------------------------
% Display the amide, rNOE, MT, and amine amplitude maps at the tumor
% slice, using fixed colormap ranges for cross-subject comparison.
% ---------------------------------------------------------------------
% Panel 5: Amide (APT) amplitude map
ax5 = nexttile(t);
min_s = 0.025; max_s = 0.075;
imshow(imrotate(amide_map(:,:,tumor_slice),90),[min_s max_s]);
colormap(ax5,'parula'); colorbar();
title('amide')
% Panel 6: Aliphatic rNOE amplitude map
ax6 = nexttile(t);
min_s = 0.06; max_s = 0.16;
imshow(imrotate(NOE_map(:,:,tumor_slice),90),[min_s max_s]);
colormap(ax6,'parula'); colorbar();
title('rNOE')
% Panel 7: MT amplitude map
ax7 = nexttile(t);
min_s = 0.045; max_s = 0.28;
imshow(imrotate(MT_map(:,:,tumor_slice),90),[min_s max_s]);
colormap(ax7,'parula');colorbar();
title('MT')
% Panel 8: Amine amplitude map
ax8 = nexttile(t);
min_s = 0.01; max_s = 0.075;
imshow(imrotate(amine_map(:,:,tumor_slice),90),[min_s max_s]);
colormap(ax8,'parula'); colorbar();
title('amine')
set(gcf,'Position',[2112 282 1118 551])
%% B0 Histogram Inset
% ---------------------------------------------------------------------
% Add a small histogram inset below the B0 map panel. Dashed red lines
% mark the ±0.2 ppm threshold used for quality assessment.
% ---------------------------------------------------------------------
inset_position = [0.01 -0.01 0.98 0.15]; % [left bottom width height] in normalized tile units
axpos = get(ax3, 'Position');
inset_position_fig = [...
axpos(1) + inset_position(1) * axpos(3),...
axpos(2) + inset_position(2) * axpos(4),...
inset_position(3) * axpos(3),...
inset_position(4) * axpos(4)]; % Convert to figure units
ax_inset = axes('Position', inset_position_fig);
histogram(ax_inset,B0_map,'EdgeColor','none','FaceColor','k','FaceAlpha',1,'BinLimits',[-0.4 0.4]);
xline(ax_inset,[-0.2 0.2],'--r')
set(ax_inset,'yticklabel',[])
%% B1 Histogram Inset
% ---------------------------------------------------------------------
% Add a small histogram inset below the B1 map panel. Dashed red lines
% mark the [0.5, 1.5] range used for quality assessment.
% ---------------------------------------------------------------------
inset_position = [0.01 -0.01 0.98 0.15]; % [left bottom width height] in normalized tile units
axpos = get(ax4, 'Position');
inset_position_fig = [axpos(1) + inset_position(1) * axpos(3),...
axpos(2) + inset_position(2) * axpos(4),...
inset_position(3) * axpos(3),...
inset_position(4) * axpos(4)]; % Convert to figure units
ax_inset = axes('Position', inset_position_fig);
histogram(ax_inset,B1_map,'EdgeColor','none','FaceColor','k','FaceAlpha',1,'BinLimits',[0.4 1.6]);
xline(ax_inset,[0.5 1.5],'--r')
set(ax_inset,'yticklabel',[])
% Save figure (requires button press to allow inspection before saving)
if save_stuff
saveas(ff,[out_folder patient_folder(ii).name],'svg')
end
%% Optional: MTR-Rex Contrast Maps
% ---------------------------------------------------------------------
% If MTR_analysis is enabled, compute and display MTR-Rex maps for
% all four CEST pools (amide, amine, rNOE, MT). MTR-Rex is defined
% as (1/Zlab - 1/Zref) and provides a quantitative measure of
% pool-specific magnetization transfer.
%
% Note: This section requires the CEST toolbox from the FAU group.
% Please contact the corresponding author if interested.
% ---------------------------------------------------------------------
if MTR_analysis
Contrasts = {'MTR_{Rex,Amide}', 'MTR_{Rex,Amine}', 'MTR_{Rex,NOE}', 'MTR_{Rex,MT}'};
Offset_list = P.SEQ.w;
[Zlab, Zref] = get_FIT_LABREF(popt,P,1,P.SEQ.w);
% Amide/APT MTR-Rex at +3.5 ppm
MTR_contrast_spectra = 1./Zlab - 1./Zref.Amide;
[contrast_offset, ~] = find_nearest(Offset_list,3.5);
MTR_contrast{1} = MTR_contrast_spectra(:,:,:,contrast_offset);
colorbar_range = [0.03 0.13];
% Amine MTR-Rex at +2.2 ppm
MTR_contrast_spectra = 1./Zlab - 1./Zref.Amine;
[contrast_offset, ~] = find_nearest(Offset_list,2.2);
MTR_contrast{2} = MTR_contrast_spectra(:,:,:,contrast_offset);
colorbar_range = [0.02 0.17];
% rNOE MTR-Rex at -3.5 ppm
MTR_contrast_spectra = 1./Zlab - 1./Zref.NOE;
[contrast_offset, ~] = find_nearest(Offset_list,-3.5);
MTR_contrast{3} = MTR_contrast_spectra(:,:,:,contrast_offset);
colorbar_range = [0 0.65];
% MT MTR-Rex at -2.0 ppm
MTR_contrast_spectra = 1./Zlab - 1./Zref.MT;
[contrast_offset, ~] = find_nearest(Offset_list,-2);
MTR_contrast{4} = MTR_contrast_spectra(:,:,:,contrast_offset);
colorbar_range = [0 0.65];
% Display MTR-Rex maps for all four pools
MTRex_f = figure(4);
MTRex_t = tiledlayout(1,4);
ax(1) = nexttile(MTRex_t);
imshow(imrotate(MTR_contrast{1}(:,:,tumor_slice),90),[0.03 0.13]);
colormap(ax(1),'parula'); colorbar();
title('amide')
ax(2) = nexttile(MTRex_t);
imshow(imrotate(MTR_contrast{3}(:,:,tumor_slice),90),[0 0.45]);
colormap(ax(2),'parula'); colorbar();
title('aliphatic rNOE')
ax(3) = nexttile(MTRex_t);
imshow(imrotate(MTR_contrast{4}(:,:,tumor_slice),90),[0.09 0.4]);
colormap(ax(3),'parula');colorbar();
title('MT')
ax(4) = nexttile(MTRex_t);
imshow(imrotate(MTR_contrast{2}(:,:,tumor_slice),90),[0.02 0.17]);
colormap(ax(4),'parula'); colorbar();
title('amine')
% Compute mean MTR-Rex per tissue ROI
MTR_amide_wm(ii) = mean(MTR_contrast{1}(logical(wm_mask)),'all','omitnan');
MTR_amine_wm(ii) = mean(MTR_contrast{2}(logical(wm_mask)),'all','omitnan');
MTR_noe_wm(ii) = mean(MTR_contrast{3}(logical(wm_mask)),'all','omitnan');
MTR_MT_wm(ii) = mean(MTR_contrast{4}(logical(wm_mask)),'all','omitnan');
MTR_amide_gm(ii) = mean(MTR_contrast{1}(logical(gm_mask)),'all','omitnan');
MTR_amine_gm(ii) = mean(MTR_contrast{2}(logical(gm_mask)),'all','omitnan');
MTR_noe_gm(ii) = mean(MTR_contrast{3}(logical(gm_mask)),'all','omitnan');
MTR_MT_gm(ii) = mean(MTR_contrast{4}(logical(gm_mask)),'all','omitnan');
MTR_amide_deep_gm(ii) = mean(MTR_contrast{1}(logical(deep_gm_mask)),'all','omitnan');
MTR_amine_deep_gm(ii) = mean(MTR_contrast{2}(logical(deep_gm_mask)),'all','omitnan');
MTR_noe_deep_gm(ii) = mean(MTR_contrast{3}(logical(deep_gm_mask)),'all','omitnan');
MTR_MT_deep_gm(ii) = mean(MTR_contrast{4}(logical(deep_gm_mask)),'all','omitnan');
MTR_amide_tumor(ii) = mean(MTR_contrast{1}(logical(tumor_mask)),'all','omitnan');
MTR_amine_tumor(ii) = mean(MTR_contrast{2}(logical(tumor_mask)),'all','omitnan');
MTR_noe_tumor(ii) = mean(MTR_contrast{3}(logical(tumor_mask)),'all','omitnan');
MTR_MT_tumor(ii) = mean(MTR_contrast{4}(logical(tumor_mask)),'all','omitnan');
% Save MTR-Rex figure
set(MTRex_f,'Position',[ 551 538 1108 254])
if save_stuff
out_folder_MTRRex = [out_folder '/MTRRex/'];
saveas(MTRex_f,[out_folder_MTRRex patient_folder(ii).name '_MTRRex'],'svg')
end
end
%% Contralateral ROI Analysis
% ---------------------------------------------------------------------
% Load manually defined contralateral (healthy) and reduced tumor ROI
% masks for slice-level comparison of CEST metrics between tumor and
% healthy tissue. These masks were defined per subject in a prior
% interactive step.
%
% The commented-out block shows the interactive mask creation workflow
% (move_mask function), retained here for reference.
% ---------------------------------------------------------------------
try
pause(0.05);
load([output_folder 'contralateral_tumor_mask.mat']);
load([output_folder 'tumor_mask_reduced.mat']);
% Display FLAIR image with reduced tumor and contralateral ROI overlays
ft = figure(100);
ax = gca;
imshow(imrotate(flair_im(:,:,tumor_slice),90),[]);
% Overlay: reduced tumor mask (red, semi-transparent)
tumor_mask_t = imrotate(double(tumor_mask_reduced),90);
red = cat(3, ones(size(tumor_mask_t)), zeros(size(tumor_mask_t)), zeros(size(tumor_mask_t)));
hold on;
auxim4 = imshow(red,'Parent',ax);
hold off;
set(auxim4,'AlphaData', 0.2.*double(tumor_mask_t./max(tumor_mask_t,[],'all')))
if save_stuff
pause(0.05);
set(ft,'Position',[2227 389 454 324])
saveas(ft,[out_folder 'reduced_tumor_mask' patient_folder(ii).name],'svg')
end
% Overlay: contralateral healthy ROI (blue, semi-transparent)
healthy_ROI = double(imrotate(new_mask,90));
blue = cat(3, zeros(size(healthy_ROI)), zeros(size(healthy_ROI)), ones(size(healthy_ROI)));
hold on;
auxim2 = imshow(blue,'Parent',ax);
hold off;
set(auxim2,'AlphaData', 0.4.*healthy_ROI)
title('Tumor segmentation CEST')
if save_stuff
pause(0.05)
set(ft,'Position',[2227 389 454 324])
saveas(ft,[out_folder 'tumor_mask_' patient_folder(ii).name],'svg');
end
catch
% Interactive mask creation workflow (disabled; retained for reference):
% if nnz(tumor_mask_certain(:,:,tumor_slice)) > 10
% [new_mask, tumor_mask_reduced] = move_mask(tumor_mask_certain(:,:,tumor_slice),flair_im(:,:,tumor_slice));
% else
% new_mask = zeros(size(tumor_mask_certain(:,:,tumor_slice)));
% tumor_mask_reduced = zeros(size(tumor_mask_certain(:,:,tumor_slice)));
% end
% save([output_folder 'contralateral_tumor_mask'],'new_mask')
% save([output_folder 'tumor_mask_reduced'],'tumor_mask_reduced')
end
% Report ROI sizes for verification
disp(['size new mask = ' num2str(nnz(new_mask))]);
disp(['size tumor mask = ' num2str(nnz(tumor_mask_reduced))]);
% Extract slice-level mean CEST amplitudes for tumor and healthy ROIs
map = amide_map(:,:,tumor_slice);
amide_tum_sli(ii) = mean(map(logical(tumor_mask_reduced)),'all','omitnan');
amide_healthyROI(ii) = mean(map(logical(new_mask)),'all','omitnan');
map = amine_map(:,:,tumor_slice);
amine_tum_sli(ii) = mean(map(logical(tumor_mask_reduced)),'all','omitnan');
amine_healthyROI(ii) = mean(map(logical(new_mask)),'all','omitnan');
map = NOE_map(:,:,tumor_slice);
noe_tum_sli(ii) = mean(map(logical(tumor_mask_reduced)),'all','omitnan');
noe_healthyROI(ii) = mean(map(logical(new_mask)),'all','omitnan');
map = MT_map(:,:,tumor_slice);
MT_tum_sli(ii) = mean(map(logical(tumor_mask_reduced)),'all','omitnan');
MT_healthyROI(ii) = mean(map(logical(new_mask)),'all','omitnan');
%% Optional: Interactive Single-Voxel Z-Spectrum Viewer
% ---------------------------------------------------------------------
% When enabled, displays the corrected Z-spectrum and its Lorentzian
% decomposition for a selected voxel, alongside the CEST image with
% the tumor mask overlaid.
% Also plots the mean Z-spectrum averaged over a combined tissue mask
% (cortical GM + WM + deep GM) at the tumor slice.
%
% This tool is intended for exploratory quality control and is
% disabled by default.
% ---------------------------------------------------------------------
interactive = 0;
if interactive
% Extract the 2D image slice at the selected slice index
image_slice = squeeze(Z_corrExt(:, :, tumor_slice, 1));
close all;
% Color palette for pool-specific Lorentzian components
figure(2);
col = [8 41 84; 224 43 53; 240 197 113; 89 168 156; 165 89 170]./255;
% Left panel: CEST image with selected voxel marker and tumor overlay
ax_aux = subplot(1, 2, 1);
imshow(image_slice, []);
x_pos = 32;
y_pos = 37;
hold on;
plot(x_pos,y_pos,'rx','MarkerSize',6)
hold on
tumor_mask_t = tumor_mask(:,:,tumor_slice);
red = cat(3, ones(size(tumor_mask_t)), zeros(size(tumor_mask_t)), zeros(size(tumor_mask_t)));
hold on;
auxim2 = imshow(red,'Parent',ax_aux);
hold off;
set(auxim2,'AlphaData', 0.2.*double(tumor_mask_t./max(tumor_mask_t,[],'all')))
% Right panel: Z-spectrum and Lorentzian decomposition for selected voxel
subplot(1, 2, 2);
I = squeeze(Z_corrExt(x_pos, y_pos, tumor_slice, :));
spectrum_plot = plot(P.SEQ.w, I,'ok','MarkerSize',4,'MarkerFaceColor','k','DisplayName','Z-Spectra');
xlim([-6 6])
ylim([0 1]);
xlabel('frequency offset (ppm)');
ylabel('I/I_0 (a.u.)')
hold on;
p = popt(x_pos,y_pos,tumor_slice,:);
w = P.SEQ.w;
% Evaluate individual pool Lorentzian contributions.
% The 7T model includes five pools with free center positions:
% pool 1 = water (direct saturation)
% pool 2 = amide
% pool 3 = MT
% pool 4 = NOE
% pool 5 = amine
pool(1,:) = p(2).*p(3).^2./4./(p(3).^2/4+(w-p(4)).^2);
pool(2,:) = p(5).*p(6).^2./4./(p(6).^2./4+(w-p(7)).^2);
pool(3,:) = p(8).*p(9).^2./4./(p(9).^2./4.+(w-p(10)).^2);
pool(4,:) = p(11).*p(12).^2./4./(p(12).^2./4.+(w-p(13)).^2);
pool(5,:) = p(14).*p(15).^2./4./(p(15).^2/4.+(w-p(16)).^2);
amp = p(1);
% Plot individual pool contributions subtracted from baseline
plot(w,amp - pool(2,:),'Linewidth',1.5,'DisplayName','Fitted amide','Color',col(1,:))
plot(w,amp - pool(3,:),'Linewidth',1.5,'DisplayName','Fitted rNOE','Color',col(2,:))
plot(w,amp - pool(4,:),'Linewidth',1.5,'DisplayName','Fitted MT','Color',col(3,:))
plot(w,amp - pool(5,:),'Linewidth',1.5,'DisplayName','Fitted amine','Color',col(5,:))
plot(w,amp - pool(1,:),'Linewidth',1.5,'DisplayName','Fitted DS','Color',col(4,:))
% Plot full five-pool fitted Z-spectrum
plot(P.SEQ.w,lorentzfit5pool(p, w, []),'k','Linewidth',1.5,'DisplayName','Fitted Z');
xlim([-6 6])
ylim([0, 1]);
xlabel('ppm');
ylabel('amplitude (a.u.)')
set(gcf,'Position',[675 685 882 277])
legend();
%% Mean Z-Spectrum Over Combined Tissue Mask
% -----------------------------------------------------------------
% Compute and display the mean Z-spectrum and its Lorentzian
% decomposition, averaged over the combined GM + WM + deep GM mask
% at the tumor slice.
% -----------------------------------------------------------------
% Build combined brain tissue mask (excluding tumor)
aux_mask = gm_mask(:,:,tumor_slice) + wm_mask(:,:,tumor_slice) + deep_gm_mask(:,:,tumor_slice);
mask_idx = find(aux_mask);
figure(3);
% Average Z-spectrum over masked voxels
Z = reshape(Z_corrExt(:,:,tumor_slice,:), [], size(Z_corrExt,4));
I = squeeze(mean(Z(mask_idx, :), 1, 'omitnan'));
spectrum_plot = plot(P.SEQ.w, I./I(1),'ok','MarkerSize',4,'MarkerFaceColor','k','DisplayName','Z-Spectra');
xlim([-6 6])
ylim([-0 1]);
xlabel('frequency offset (ppm)');
ylabel('Amplitude (a.u.)')
hold on;
% Average fitted parameters over masked voxels
popt_slice = reshape(popt(:,:,tumor_slice,:), [], size(popt,4));
p = squeeze(mean(popt_slice(mask_idx, :), 1, 'omitnan'));
w = P.SEQ.w;
pool(1,:) = p(2).*p(3).^2./4./(p(3).^2/4+(w-p(4)).^2);
pool(2,:) = p(5).*p(6).^2./4./(p(6).^2./4+(w-p(7)).^2);
pool(3,:) = p(8).*p(9).^2./4./(p(9).^2./4.+(w-p(10)).^2);
pool(4,:) = p(11).*p(12).^2./4./(p(12).^2./4.+(w-p(13)).^2);
pool(5,:) = p(14).*p(15).^2./4./(p(15).^2/4.+(w-p(16)).^2);
amp = p(1);
% Plot normalized pool contributions and full fit
plot(w,(amp - pool(2,:))./I(1),'Linewidth',1.5,'DisplayName','Fitted amide','Color',col(1,:))
plot(w,(amp - pool(3,:))./I(1),'Linewidth',1.5,'DisplayName','Fitted rNOE','Color',col(2,:))
plot(w,(amp - pool(4,:))./I(1),'Linewidth',1.5,'DisplayName','Fitted MT','Color',col(3,:))
plot(w,(amp - pool(5,:))./I(1),'Linewidth',1.5,'DisplayName','Fitted amine','Color',col(5,:))
plot(w,(amp - pool(1,:))./I(1),'Linewidth',1.5,'DisplayName','Fitted DS','Color',col(4,:))
plot(P.SEQ.w,lorentzfit5pool(p, w, [])./I(1),'k','Linewidth',1.5,'DisplayName','Fitted Z');
xlim([-6 6])
ylim([0, 1]);
xlabel('frequency offset (ppm)');
ylabel('Amplitude (a.u.)')
legend();
end
end % end of per-subject loop
%% Save Intermediate Results
% -------------------------------------------------------------------------
% Save the workspace to disk after the main subject loop. This checkpoint
% allows the exclusion and export steps below to be run independently.
% -------------------------------------------------------------------------
if save_stuff
close all;
save([out_folder '7T_results'])
end
% To reload from checkpoint: load([out_folder '7T_results'])
%% Subject Exclusion
% -------------------------------------------------------------------------
% Subjects are excluded from group analysis based on four criteria:
% 1) Manual exclusion due to poor segmentation quality or acquisition
% issues (exc_sub). Subject 3 was excluded due to a non-standard
% image orientation.
% 2) Excessive tumor volume relative to brain volume (> 25%)
% 3) Excessive B0 inhomogeneity: > 20% of voxels outside ±0.4 ppm
% 4) Excessive B1 inhomogeneity: > 20% of voxels outside [0.5, 1.5]
%
% All metrics for excluded subjects are set to NaN.
% Further details on each exclusion are provided in the paper and in the
% shared patient table.
% -------------------------------------------------------------------------
exc_sub = [3 14 33]; % Subject 3: non-standard orientation
tumor_exc = find(tumor_per > 25);
B0_exc = find(B0_per > 20);
B1_exc = find(B1_per > 20);
exclude = unique([exc_sub B0_exc B1_exc tumor_exc]);
for ii = exclude
patient_table.age(ii) = nan;
amide_wm(ii) = nan;
amine_wm(ii) = nan;
noe_wm(ii) = nan;
MT_wm(ii) = nan;
amide_gm(ii) = nan;
amine_gm(ii) = nan;
noe_gm(ii) = nan;
MT_gm(ii) = nan;
amide_tumor(ii) = nan;
amine_tumor(ii) = nan;
noe_tumor(ii) = nan;
MT_tumor(ii) = nan;
amide_hip(ii) = nan;
amine_hip(ii) = nan;
noe_hip(ii) = nan;
MT_hip(ii) = nan;
amide_deep_gm(ii) = nan;
amine_deep_gm(ii) = nan;
noe_deep_gm(ii) = nan;
MT_deep_gm(ii) = nan;
amide_global(ii) = nan;
amine_global(ii) = nan;
noe_global(ii) = nan;
MT_global(ii) = nan;
end
%% Export Summary Tables
% -------------------------------------------------------------------------
% Assemble and optionally save two output tables:
% 1) table_7T (full): Full tissue-level CEST metrics per subject.
% 2) table_7T (tumor): Slice-level tumor vs. contralateral healthy ROI.
% -------------------------------------------------------------------------
% Table 1: Full ROI summary (all tissue types and CEST pools)
tumor_type = cell2mat(patient_table.WHO_grade)';
varNames = ["age","sex","tumor_type","amide_wm","amine_wm","noe_wm","MT_wm","amide_gm","amine_gm","noe_gm","MT_gm","amide_tumor","amine_tumor","noe_tumor","MT_tumor","amide_deep_gm","amine_deep_gm","noe_deep_gm","MT_deep_gm"];
table_7T = table(patient_table.age,patient_table.sex,tumor_type',amide_wm',amine_wm',noe_wm',MT_wm',amide_gm',amine_gm',noe_gm',MT_gm',amide_tumor',amine_tumor',noe_tumor',MT_tumor',amide_deep_gm',amine_deep_gm',noe_deep_gm',MT_deep_gm','VariableNames',varNames);
if save_stuff
writetable(table_7T,[out_folder,'table_7T'])
end
% Table 2: Tumor slice — tumor ROI vs. contralateral healthy ROI
varNames = ["age","amide_tum","amide_healthy","amine_tum","amine_healthy","noe_tum","noe_healthy","MT_tum","MT_healthy"];
table_7T = table(patient_table.age,amide_tum_sli',amide_healthyROI',amine_tum_sli',amine_healthyROI',noe_tum_sli',noe_healthyROI',MT_tum_sli',MT_healthyROI','VariableNames',varNames);
if save_stuff
writetable(table_7T,[out_folder,'table_tumor_7T'])
end
%% Local Functions
% =========================================================================
function updateSpectrum(src, event, data, slice, spectrum_plot)
% updateSpectrum Update the Z-spectrum display based on the cursor position.
%
% Intended for use as a WindowButtonMotionFcn callback. Updates the
% spectrum_plot line object with the Z-spectrum at the voxel under
% the cursor.
%
% Inputs:
% src - Figure handle (unused, required by callback API)
% event - Event data (unused, required by callback API)
% data - 4D CEST data array [x, y, z, offsets]
% slice - Current axial slice index
% spectrum_plot - Handle to the line object to update
current_point = get(gca, 'CurrentPoint');
x = round(current_point(1, 1));
y = round(current_point(1, 2));
if x >= 1 && x <= size(data, 1) && y >= 1 && y <= size(data, 2)
spectrum = squeeze(data(x, y, slice, :));
set(spectrum_plot, 'YData', spectrum);
title(spectrum_plot.Parent,['Spectrum at (' num2str(x) ', ' num2str(y) ', ' num2str(slice) ')']);
end
end

Plot_Result_7T.m at commit 8997a6f, under MIT · at the source

Overview

Authors: Milena Capiglioni^1,2,3, Moritz Simon Fabian⁴, Stefanie Marti⁵, Richard McKinley¹, Robert Hoepner⁵, Alejandro León Betancourt⁵, Johannes Slotboom^1,2, Roland Wiest^1,2, Moritz Zaiss^4,6, Angelika Mennecke⁴, Piotr Radojewski^1,2

ORCID iDs: Milena Capiglioni, Stefanie Marti, Johannes Slotboom, Moritz Zaiss

Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging, University of Bern, 3010 Bern, Switzerland
Translational Imaging Center (TIC), Sitem-insel, 3010 Bern, Switzerland
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
Department of Neurology, Inselspital, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany

Institutions: University of Bern (Switzerland); Max Planck Institute for Biological Cybernetics (Germany); Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany); Universitätsklinikum Erlangen (Germany); University Hospital of Bern (Switzerland)

Journal: Brain communications, volume 8, issue 4, article fcag248

Dates: received 25 July 2025; accepted 29 April 2026; published online 30 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1093/braincomms/fcag248 · PMID 42428750 · PMCID PMC13348846 · OpenAlex W7166552870

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), other condition (population), clinical / translational (subfield)

Methods: Statistics, Preprocessing

Keywords: CEST, ageing biomarker, ultra-high field, glioma, tumour subtyping

Topic: Lanthanide and Transition Metal Complexes (Materials Chemistry, Materials Science), according to OpenAlex

Funding: Swiss National Science Foundation (SNSF-182569, 182569); Swiss Multiple Sclerosis Society (2023-22)

Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

Chemical exchange saturation transfer (CEST) MRI provides insight into tissue metabolism by detecting low-concentration endogenous molecules. While studies at 7 Tesla (7T) have shown enhanced sensitivity and spectral separation, 3 Tesla (3T) remains the clinical standard, and the relative performance of these field strengths in a direct clinical head-to-head comparison remains unclear. This prospective cohort study provides a direct within-subject comparison of multi-pool CEST imaging at 3T and 7T, using age-related tissue changes and glioma molecular subtypes as representative applications of physiological and pathological CEST sensitivity. Forty-three patients (ages 18–76; 18 female) underwent 3T and 7T CEST MRI prior to surgery due to suspected brain tumour; following quality control, 36 datasets were included at 3T and 32 at 7T. CEST amplitudes from amide, amine, aliphatic relayed nuclear Overhauser effect (rNOE) and magnetization transfer pools were quantified in white matter, grey matter, deep grey matter and tumour tissue. A physics-informed conditional autoencoder (PICAE) was applied at 7T to correct B1 inhomogeneity. Age effects were tested using linear regression; tumour subtype differences were tested using the Wilcoxon rank-sum test. The significance level was set to α = 0.05, and the Holm–Bonferroni procedure was applied to correct for multiple testing. At 7T, amide and rNOE showed robust negative correlations with age in grey matter and deep grey matter, supporting the potential of CEST as an ageing biomarker. Age dependence at 3T was weaker, limited to rNOE (grey matter and deep grey matter) and magnetization transfer (white matter and deep grey matter). In contrast, tumour CEST metrics showed no significant age dependence at either field strength. Trends in relative amide contrast were consistent with prior findings but did not reach statistical significance. Sensitivity to tumour molecular subtype was similar across field strengths. Variability analyses showed that conventional 7T processing introduced higher technical variability than 3T, whereas PICAE substantially reduced variability and improved data quality at 7T. In conclusion, 7T CEST MRI demonstrates higher potential as a non-invasive marker of brain ageing, whereas our simplified pipeline did not yield additional information for tumour subtyping at either field strength. These findings underscore both the enhanced sensitivity and the higher technical demands of 7T, and highlight the importance of advanced correction strategies such as PICAE for robust use of single-transmit 7T CEST.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 21 matches between paragraphs and lines of code.

milecap/GliomaCEST-3T7T

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 8997a6f8110f8441b2024c3cfddf6c37a83b83f1, 20 April 2026

Languages: MATLAB (24)

Size: 32 files, 24 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (4 files), Tools for NIfTI and ANALYZE image (MATLAB) (4 files), Image Processing Toolbox (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

26 files

Plot_Result_3T.m, MATLAB, 1,064 lines, 6 matches
Plot_Result_7T.m, MATLAB, 1,144 lines, 7 matches
Statistical_analysis_3T.m, MATLAB, 494 lines, 3 matches
Statistical_analysis_7T.m, MATLAB, 458 lines, 1 match
aux_scripts/getPathToFileContainingString.m, MATLAB, 44 lines
aux_scripts/lorentzfit5pool.m, MATLAB, 5 lines
aux_scripts/nii_func/load_nii.m, MATLAB, 138 lines
aux_scripts/nii_func/load_nii_ext.m, MATLAB, 148 lines
aux_scripts/nii_func/load_nii_hdr.m, MATLAB, 320 lines
aux_scripts/nii_func/load_nii_img.m, MATLAB, 385 lines
aux_scripts/nii_func/load_untouch0_nii_hdr.m, MATLAB, 200 lines
aux_scripts/nii_func/load_untouch_nii.m, MATLAB, 191 lines
aux_scripts/nii_func/load_untouch_nii_hdr.m, MATLAB, 217 lines
aux_scripts/nii_func/load_untouch_nii_img.m, MATLAB, 468 lines
aux_scripts/nii_func/make_nii.m, MATLAB, 218 lines
aux_scripts/nii_func/save_nii.m, MATLAB, 234 lines
aux_scripts/nii_func/save_nii_ck.m, MATLAB, 235 lines
aux_scripts/nii_func/save_nii_ext.m, MATLAB, 38 lines
aux_scripts/nii_func/save_nii_hdr.m, MATLAB, 239 lines
aux_scripts/nii_func/save_untouch0_nii_hdr.m, MATLAB, 219 lines
aux_scripts/nii_func/save_untouch_nii.m, MATLAB, 232 lines
aux_scripts/nii_func/save_untouch_nii_hdr.m, MATLAB, 207 lines
aux_scripts/nii_func/verify_nii_ext.m, MATLAB, 45 lines
comparison_tumour_3T_7T.m, MATLAB, 559 lines, 4 matches
LICENSE, License, 21 lines
README.md, Text, 52 lines

At the source: github.com/milecap/GliomaCEST-3T7T.git

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;
24 scripts, each with its path and the digest of its content;
21 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

All raw and processed chemical exchange saturation transfer brain imaging data from this study will be openly available upon publication via the University of Bern data repository: https://doi.org/10.48620/96500. The corresponding processing and analysis code is available at: https://github.com/milecap/GliomaCEST-3T7T.git

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 2 funders, 56 references.

Cite

This paper

Capiglioni, M., Fabian, M. S., Marti, S., McKinley, R., Hoepner, R., Betancourt, A. L., Slotboom, J., Wiest, R., Zaiss, M., Mennecke, A., & Radojewski, P. (2026). Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort. Brain communications, 8(4), fcag248. https://doi.org/10.1093/braincomms/fcag248

BibTeX

@article{capiglioni2026field, author = {Capiglioni, Milena and Fabian, Moritz Simon and Marti, Stefanie and McKinley, Richard and Hoepner, Robert and Betancourt, Alejandro León and Slotboom, Johannes and Wiest, Roland and Zaiss, Moritz and Mennecke, Angelika and Radojewski, Piotr}, title = {{Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort}}, journal = {Brain communications}, year = {2026}, month = jun, volume = {8}, number = {4}, pages = {fcag248}, publisher = {Oxford University Press}, issn = {2632-1297}, doi = {10.1093/braincomms/fcag248}, url = {https://doi.org/10.1093/braincomms/fcag248}, pmid = {42428750}, pmcid = {PMC13348846} }

RIS

TY - JOUR AU - Capiglioni, Milena AU - Fabian, Moritz Simon AU - Marti, Stefanie AU - McKinley, Richard AU - Hoepner, Robert AU - Betancourt, Alejandro León AU - Slotboom, Johannes AU - Wiest, Roland AU - Zaiss, Moritz AU - Mennecke, Angelika AU - Radojewski, Piotr TI - Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort T2 - Brain communications J2 - Brain Commun PY - 2026 DA - 2026/06/30 VL - 8 IS - 4 SP - fcag248 SN - 2632-1297 PB - Oxford University Press DO - 10.1093/braincomms/fcag248 UR - https://doi.org/10.1093/braincomms/fcag248 LA - en ER -

CSL-JSON

{ "id": "10.1093/braincomms/fcag248", "type": "article-journal", "title": "Field strength-dependent sensitivity of chemical exchange saturation transfer: a methodological comparison of 3 Tesla and 7 Tesla in a clinical cohort", "container-title": "Brain communications", "author": [ { "family": "Capiglioni", "given": "Milena" }, { "family": "Fabian", "given": "Moritz Simon" }, { "family": "Marti", "given": "Stefanie" }, { "family": "McKinley", "given": "Richard" }, { "family": "Hoepner", "given": "Robert" }, { "family": "Betancourt", "given": "Alejandro León" }, { "family": "Slotboom", "given": "Johannes" }, { "family": "Wiest", "given": "Roland" }, { "family": "Zaiss", "given": "Moritz" }, { "family": "Mennecke", "given": "Angelika" }, { "family": "Radojewski", "given": "Piotr" } ], "container-title-short": "Brain Commun", "volume": "8", "issue": "4", "page": "fcag248", "DOI": "10.1093/braincomms/fcag248", "PMID": "42428750", "PMCID": "PMC13348846", "ISSN": "2632-1297", "publisher": "Oxford University Press", "URL": "https://doi.org/10.1093/braincomms/fcag248", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 30 ] ] } }

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