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pm7_Spatial_processing_THK5351_MR.m
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function Spatial_processing_PET_THK5351_flutemetamol_Part_II(radiotracer)
%
% Script Part II: spatial processing of PET data of two radiotracers
% (THK5351 and flutemetamol). The script contains: loading of data (PET
% sumframe, MT structural MRI image and AAL atlas), coregistration of the
% PET to the MT image, normalisation of the PET using the normalisation
% parameters obtained during the normalisation of the MT image (see
% Spatial_processing_MPM script), extraction of the regional uptake and
% creation of RSUV image.
% THK5351: RSUV image was obtained by dividing the normalised pve corrected
% PET image by the mean uptake of the cerebelum GM.
% Text file containing the cerebelum GM mean uptake values.
% Flutemetamol: text file containing all AAL rois uptake values.
%
% M.Bahri: 2017/09/04
% -------------------------------------------------------------------------
% SPM-style file renaming (pm 20180412)
% image PET originale = rnsCOF*40-60.nii
% image PET originale + coregister PET to MT image = rrnsCOF*40-60
% image PET originale + coregister + petpvc = PrrnsCOF*40-60_IY
% image PET originale + coregister + petpvc + normalization with MPM parameters = wPrrnsCOF*40-60
% image PET originale + coregister + petpvc + normalization + SUV = SUVwPrrns*40-60
% image PET originale + coregister + petpvc + normalization + SUV + division par mean WM = SUVRwPrrns*40-60
% =========================================================================
% Radiotracer: THK5351 = 1; Flutemetamol = 2
global data;
spm pet;
root_pth ='D:\thk';
addpath('D:\thk\thk_codes')
% =========================================================================
%% cd: corrected fsl config
% in matlab system('bash -c <fsl_command>'), .bashrc which calls fsl_config_script is not executed
% thus the way to do it is system('bash -c <caller_shell_script fsl_command>')
% caller_shell_script (bash_call.sh in ~/code calls fsl_config_script, then executes fsl_command
%
if ispc,
% bash_call.sh no longer in thk_code directory, moved to ~/code
% bash_call_file=fullfile(fileparts(mfilename('fullpath')),'bash_call.sh')
% prefix=['bash -c "' bash_call_file '" '];
prefix=['bash -c "~/code/bash_call.sh '];
suffix='"';
else
prefix='';
suffix='';
end;
% -cd
%%
% =========================================================================
%
data = pm0_COF_data(fullfile(root_pth));
% Open output text file
% switch radiotracer
% case 1
% =========================================================================
% Load Cerebelum grey mater mask
Refroi_CerebGM = fullfile(root_pth,'thk_codes','erodeRefroi_CerebGM_MNI.nii');
ma = spm_vol(Refroi_CerebGM);
mask = spm_read_vols(ma);
ind = find(abs(mask) == 9);
% Open a text file
fid = fopen(fullfile(root_pth,'thk_codes','PET_CerebelumGM_values.txt'), 'w');
fprintf(fid,'Subject_id\t');
fprintf(fid,'%s\t','CereblumGM(Bq/cc)');
% case 2
aal2_atlas = fullfile(spm('Dir'),'toolbox','aal','ROI_MNI_V5.nii');
aal2_rois = fullfile(spm('Dir'),'toolbox','aal','ROI_MNI_V5.txt');
% Read atlas file
atlas_img = spm_vol(aal2_atlas);
Vol_atlas = spm_read_vols(atlas_img);
mask_values = round(unique(Vol_atlas));
Vol_atlasRounded = round(Vol_atlas);
% get rois names
roisfile = readcoglog(aal2_rois);
% open a text file
fidout = fopen(fullfile(root_pth,'PET_aalROIs_values.txt'), 'w');
fprintf(fidout,'Subject_id\t')
for roi=1:length(roisfile)
fprintf(fidout,'%s\t',char(roisfile{roi}(2)));
end
fprintf(fidout,'%s\t%s\t%s','GM','WM','CSF');
% end
% =========================================================================
for isub =setdiff([1:size(data,2)],3) %[4 14 23 32 39 41 44 45] %
fprintf(1,'PROCESSING SUBJECT %i / %i : %s\n',isub,size(data,2),data(isub).id)
% check for MRI and PET directories
if exist(fullfile(root_pth,data(isub).id,'PET'),'dir')==0 || isempty(data(isub).thkid)
fprintf(1,'Pas de PET pour %s\n',data(isub).id)
elseif exist(fullfile(root_pth,data(isub).id,'MRI','nii2','Results'),'dir')==0
fprintf(1,'Pas de MRI pour %s\n',data(isub).id)
elseif exist(fullfile(root_pth,data(isub).id,'MRI','nii2','Results'),'dir')==7
% load segmented tissues (c1... c6)
Segmaps = get_Segmaps(isub);
MTmap = get_MTmap(isub);
PETfile = get_PETfile(isub);
Normdef = get_Normdef(isub);
%% cd: make sure we have unix filepaths
c4c5c6=fullfile(data(isub).dir,'MRI','nii2','c4c5c6.nii');
mask4d=fullfile(data(isub).dir,'MRI','nii2','Mask4D.nii');
fprintf(1,'calling fsl\n')
if ispc,
for i=1:6,
fprintf(1,'Press return\n')
[s r]=system(['bash -c ''~/code/wslpath -a -u "' Segmaps{i} '"''']);
fprintf(1,'Call successful for %s\n',Segmaps{i})
r(end)='';
unix_Segmaps{i}=r;
end
fprintf(1,'Press return\n')
[s r]=system(['bash -c ''~/code/wslpath -a -u "' c4c5c6 '"''']); r(end)='';
fprintf(1,'Call successful for %s\n','c4c5c6')
unix_c4c5c6=r;
fprintf(1,'Press return\n')
[s r]=system(['bash -c ''~/code/wslpath -a -u "' mask4d '"''']); r(end)='';
fprintf(1,'Call successful for %s\n','mask4d')
unix_mask4d=r;
else
unix_Segmaps=Segmaps;
unix_c4c5c6=c4c5c6;
unix_mask4d=mask4d;
end
% -cd
%%
fprintf(1,'Compute fsl masks\n')
% Calculate the sum of the tissues c4, c5, c6 using fsl command line
fprintf(1,'Press return\n')
system([prefix 'fslmaths ' unix_Segmaps{4} ' -add ' unix_Segmaps{5} ' -add ' unix_Segmaps{6} ' ' unix_c4c5c6 suffix]);
fprintf(1,'Call successful for %s\n','fslmaths')
% Create the 4D mask containing c1,c2,c3, and the background including
% c4,c5,and c6.
fprintf(1,'Press return\n')
system([prefix 'fslmerge -t ' unix_mask4d ' ' unix_Segmaps{1} ' ' unix_Segmaps{2} ' ' unix_Segmaps{3} ' ' unix_c4c5c6 suffix]);
fprintf(1,'Call successful for %s\n','fslmerge')
% =========================================================================
% Coregister PET into MRI MT image using SPM
[pth,nam,ext] = fileparts(PETfile);
matlabbatch{1}.spm.spatial.coreg.estwrite.ref = cellstr(MTmap);
matlabbatch{1}.spm.spatial.coreg.estwrite.source = cellstr(PETfile);
spm_jobman('run',matlabbatch)
clear matlabbatch;
% =========================================================================
%% cd: make sure we have unix filepaths
fprintf(1,'Compute pvc\n')
petpvc_in=fullfile(pth,['r' nam ext]);
petpvc_out=fullfile(pth,['r' nam '_IY' ext]);
if ispc,
fprintf(1,'Press return\n')
[s r]=system(['bash -c ''~/code/wslpath -a -u "' petpvc_in '"''']); r(end)='';
fprintf(1,'Call successful for %s\n','petpvc_in')
unix_petpvc_in=r;
fprintf(1,'Press return\n')
[s r]=system(['bash -c ''~/code/wslpath -a -u "' petpvc_out '"''']); r(end)='';
fprintf(1,'Call successful for %s\n','petpvc_out')
unix_petpvc_out=r;
else
unix_petpvc_in=petpvc_in;
unix_petpvc_out=petpvc_out;
end
% -cd
%%
% Partial volume correction using PETPVC toolbox --> rrns
system([prefix 'petpvc -i ' unix_petpvc_in ' -m ' unix_mask4d ' -o ' unix_petpvc_out ' --pvc IY -x 6.0 -y 6.0 -z 6.0' suffix]);
fprintf(1,'Call successful for %s\n','petpvc')
% Rename PET image with petpvc following SPM filenaming (prefixing)
movefile(fullfile(pth,['r' nam '_IY' ext]),fullfile(pth,['Pr' nam ext]))
% Normalize PET & PETpvc images using MPM normalization parameters
matlabbatch{1}.spm.spatial.normalise.write.subj.def = cellstr(Normdef)
matlabbatch{1}.spm.spatial.normalise.write.subj.resample = {fullfile(pth,['r' nam ext]);fullfile(pth,['Pr' nam ext])};
% Bounding box, voxel size and interpolation options could be modified
matlabbatch{1}.spm.spatial.normalise.write.woptions.bb = [-78 -112 -70;78 76 85];
matlabbatch{1}.spm.spatial.normalise.write.woptions.vox = [2 2 2];
matlabbatch{1}.spm.spatial.normalise.write.woptions.interp = 4;
spm_jobman('run',matlabbatch)
clear matlabbatch;
% =========================================================================
% Create a temporary PET image with same dimension as the AAL atlas
matlabbatch{1}.spm.util.imcalc.input = {aal2_atlas;fullfile(pth,['wr' nam '_IY' ext])};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['tmpwr' nam '_IY' ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = 'i2';
spm_jobman('run',matlabbatch);
clear matlabbatch;
switch radiotracer
case 1
fprintf(fidout,'\n%s\t%s',data(isub).id,'Radiotracer: THK5153');
% % Extract Cerebelum GM value for each subject
% Ma = spm_vol(fullfile(pth,['tmpwr' nam '_IY' ext]));
% subj_vol = spm_read_vols(Ma);
% % Calculate mean value of Cerebelum GM
% region_mean = mean(subj_vol(ind));
% fprintf(fid,'\n%s\t', char(data(isub).id));
% fprintf(fid,'%d\t', region_mean);
% % Normalized PET division by value in ROI Cerebelum GM
% f = strcat('i1 ./ ',num2str(region_mean));
% value1 = round(region_mean);
%
% matlabbatch{1}.spm.util.imcalc.input = {fullfile(pth,['wr' nam '_IY' ext])};
% matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['wr' nam '_IY' strcat('_divROI',num2str(value1)) ext]);
% matlabbatch{1}.spm.util.imcalc.outdir = {pth};
% matlabbatch{1}.spm.util.imcalc.expression = f;
%
% spm_jobman('run',matlabbatch);
% clear matlabbatch;
% =========================================================================
% Create SUV image (Normalized PET is divided by injected dose and patient
% weight) from wrrn and wPrrn images
value = data(isub).Dose/data(isub).weight*1000; %(*1000 if dose in MBq)
f = strcat('i1 ./ ',num2str(value));
matlabbatch{1}.spm.util.imcalc.input = {fullfile(pth,['wr' nam ext])};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['SUVwr' nam ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = f;
spm_jobman('run',matlabbatch);
clear matlabbatch;
matlabbatch{1}.spm.util.imcalc.input = {fullfile(pth,['wPr' nam ext])};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['SUVwPr' nam ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = f;
spm_jobman('run',matlabbatch);
clear matlabbatch;
% =========================================================================
% Create SUVR image by division by grey matter mask
% pm 20180325
% load individual white matter mask
Mask=spm_select('FPList',fullfile(data(isub).dir,'MRI','nii2','Results'),strcat('^wc2s',data(isub).LongMRI,'.+\.nii$'));
Vmask = spm_vol(Mask);
[Y,XYZmm] = spm_read_vols(Vmask);
Ind = intersect(find(Y>0.9), find(XYZmm(3,:)>0)); % WM above ACPC plane
%load image wo petpvc
IndivPET=spm_select('FPList',pth,['SUVwr' nam ext]);
Vin = spm_vol(IndivPET);
[Y,XYZmm] = spm_read_vols(Vmask);
region_mean = mean(Y(Ind));
f = strcat('i1 ./ ',num2str(region_mean));
matlabbatch{1}.spm.util.imcalc.input = {IndivPET};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['SUVRwr' nam ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = f;
spm_jobman('run',matlabbatch);
clear matlabbatch region_mean;
%load image with petpvc
IndivPET=spm_select('FPList',pth,['SUVwPr' nam ext]);
Vin = spm_vol(IndivPET);
[Y,XYZmm] = spm_read_vols(Vmask);
region_mean = mean(Y(Ind));
f = strcat('i1 ./ ',num2str(region_mean));
matlabbatch{1}.spm.util.imcalc.input = {IndivPET};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['SUVRwPr' nam ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = f;
spm_jobman('run',matlabbatch);
clear matlabbatch region_mean;
case 2
fprintf(fidout,'\n%s\t%s',data(isub).id,'Radiotracer: Flutemetamol');
% Extract AAL ROIs mean values for each subject
Ma = spm_vol(fullfile(pth,['tmpwr' nam '_IY' ext]));
subj_vol = spm_read_vols(Ma);
for i = 1:length(roisfile)
region_mean(i) = mean(subj_vol(ind2sub(size(Vol_atlas),find(Vol_atlasRounded == str2num(char(roisfile{i}(3)))))));
fprintf(fidout,'%d\t', region_mean(i));
end
% Extract GM, WM, and CSF mean PET values. These values were
% extracted from the PET pve corrected image (rrns
% ...._IY.nii).
Ma = spm_vol(fullfile(pth,['r' nam '_IY' ext]));
subj_vol = spm_read_vols(Ma);
for i = 1:3
Ma = spm_vol(Segmaps{i});
sub_vol = spm_read_vols(Ma);
index = find(sub_vol);
mask_mean(i) = mean(subj_vol(index));
fprintf(fidout, '%d\t',mask_mean(i));
end
% Create SUV image (Normalized PET is divided by injected dose
% and patient weight)
value = str2num(data(isub).dose)/str2num(data(isub).weight)*1000; %(*1000 if dose in MBq)
f = strcat('i1 ./ ',num2str(value));
matlabbatch{1}.spm.util.imcalc.input = {fullfile(pth,['wr' nam '_IY' ext])};
matlabbatch{1}.spm.util.imcalc.output = fullfile(pth,['wr' nam '_IY_SUV' ext]);
matlabbatch{1}.spm.util.imcalc.outdir = {pth};
matlabbatch{1}.spm.util.imcalc.expression = f;
spm_jobman('run',matlabbatch);
clear matlabbatch;
end
end
end
status = fclose(fid);
% =========================================================================
% Functions
% =========================================================================
function [MTmap] = get_MTmap(isub);
global data
souname = data(isub).LongMRI;
dirstruc = [];
dirstruc = fullfile(data(isub).dir,'MRI','nii2','Results');
[MTmap]=spm_select('FPList',dirstruc,strcat('^s',souname,'-.+_MT.nii$'));
return
function [Segmaps] = get_Segmaps(isub);
global data
souname = data(isub).LongMRI;
dirstruc = [];
dirstruc = fullfile(data(isub).dir,'MRI','nii2','Results');
for ii=1:6
Segmaps{ii}=spm_select('FPList',dirstruc,strcat('^c',num2str(ii),'s',souname,'-.+_MT.nii$'));
end
return
function [PETfile] = get_PETfile(isub);
global data
souname = data(isub).id;
dirstruc = [];
dirstruc = fullfile(data(isub).dir,'PET');
[PETfile]=spm_select('FPList',dirstruc,strcat('^rns',souname,'_.+\.nii$'));
return
function [Normdef] = get_Normdef(isub);
global data
souname = data(isub).LongMRI;
dirstruc = [];
dirstruc = fullfile(data(isub).dir,'MRI','nii2','Results');
[Normdef]=spm_select('FPList',dirstruc,strcat('^y_s',souname,'-.+_MT.nii$'));
return