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scott-love · Jun 2, 2017 · 74c091f · 74c091f
1 parent 801cd9e
commit 74c091f
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Showing 2 changed files with 14 additions and 7 deletions.
diff --git a/svca4_calculate.m b/svca4_calculate.m
@@ -52,13 +52,13 @@
     % vectorizing target for speed
     PET_vector = reshape(PET_norm, xDim*yDim*zDim, svca4.nFrames);
     % add last row of ones to improve optimisation 
-    CLASS(end+1,:) = 1;
+    %CLASS(end+1,:) = 1;
     for j = 1:size(PET_vector,1);
         if MASK(j) > 0
             TAC = squeeze(PET_vector(j,:)');
             TAC(isnan(TAC)) = 0;
             % fitting
-            TAC(end+1,:) = 1;
+            %TAC(end+1,:) = 1;
             par = lsqnonneg(CLASS,TAC);
 
             % filling parametric maps

diff --git a/svca4_calculate_classes.m b/svca4_calculate_classes.m
@@ -36,12 +36,12 @@
 
     %%% load brain mask %%%
     MASK_struct = load_nii(fullfile(svca4.MASK_dir, svca4.MASK_list{fi}));
-    MASK = single(MASK_struct.img);
+    MASK = MASK_struct.img;
     %clear MASK_struct
 
     %%% load PET image %%%
     PET_struct = load_nii(fullfile(svca4.PET_dir, svca4.PET_list{fi}));
-    PET = single(PET_struct.img);
+    PET = PET_struct.img;
     svca4.Res = PET_struct.hdr.dime.pixdim([2 4 3]); %
     xDim = size(PET,1);
     yDim = size(PET,2);
@@ -53,13 +53,17 @@
     PET_norm = zeros(xDim,yDim,zDim,svca4.nFrames);
     for t=1:svca4.nFrames
         PET_t = PET(:,:,:,t);
+        PET_mask(:,:,:,t) = PET_t.*MASK;
         vals  = PET_t(indMASK) - mean(PET_t(indMASK));
         if std(vals(:)) ~= 0
             vals = vals/std(vals(:));
+        else vals = vals/1; % keep away the NaN
         end
         PET_t_norm = PET_norm(:,:,:,t);
         PET_t_norm(indMASK) = vals;
         PET_norm(:,:,:,t) = PET_t_norm;
+        meanBrain(t) = mean(PET_t(indMASK));
+        stdBrain(t) = std(PET_t(indMASK));
     end
 
 
@@ -72,20 +76,23 @@
             BANANA = single(BANANA_struct.img); clear BANANA_struct
             BM4D = repmat(BANANA, [1 1 1 numel(svca4.BLOOD_frames)]);
             firstFrames = PET_norm(:,:,:,svca4.BLOOD_frames).*single(BM4D);
-        else firstFrames = PET_norm(:,:,:,svca4.BLOOD_frames);
+        else %firstFrames = PET_norm(:,:,:,svca4.BLOOD_frames);
+            firstFrames = PET_mask(:,:,:,svca4.BLOOD_frames);
         end
 
         vox_tm_max = max(firstFrames, [], 4);
 
         BLOOD = zeros(1,svca4.nFrames);
         BLOODnn = zeros(1,svca4.nFrames);
         for j=1:svca4.BLOOD_num_pixels
-            [~, ind] = max(vox_tm_max(:));
-            [indx, indy, indz] = ind2sub([xDim yDim zDim], ind);
+            [vv(j), ind(j)] = max(vox_tm_max(:));
+            [indx, indy, indz] = ind2sub([xDim yDim zDim], ind(j));
             BLOOD = BLOOD + squeeze(PET_norm(indx,indy,indz,1:svca4.nFrames))';
+            allBlood(j,:) = squeeze(PET_mask(indx,indy,indz,1:svca4.nFrames))';
             BLOODnn = BLOODnn + squeeze(PET(indx,indy,indz,1:svca4.nFrames))';
             vox_tm_max(indx, indy, indz) = 0;
         end
+
         TAC_TABLE(fi,3,1:svca4.nFrames) = squeeze(BLOOD/svca4.BLOOD_num_pixels);
         TAC_TABLEnn(fi,3,1:svca4.nFrames) = squeeze(BLOODnn/svca4.BLOOD_num_pixels);
     end