-
Notifications
You must be signed in to change notification settings - Fork 0
/
mems_fmri_run.m
228 lines (156 loc) · 7.41 KB
/
mems_fmri_run.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
function [out]=mems_fmri_run(job)
warnstate = warning;
warning off;
spm_defaults;
nechoes=numel(job.tedat);
ntime=numel(job.tedat(1).func);
out=cell(ntime,1);
for ie=1:nechoes
te(ie) = job.tedat(ie).te;
for it=1:ntime
Vfunc=spm_vol(job.tedat(ie).func{it});
if ie==1 && it==1
voldim = Vfunc.dim;
tefuncdat = zeros(voldim(1),voldim(2),voldim(3),ntime,nechoes);
end
tefuncdat(:,:,:,it,ie) = spm_read_vols(Vfunc);
end
end
funcdat = zeros(voldim(1),voldim(2),voldim(3),ntime);
switch job.method
case 0 %Average (wi=1/nechoes)
funcdat = sum(tefuncdat,5) ./ nechoes;
cmethod = 'average';
case 1 %BS (wi=TEi/sum(TEi))
sum_weights = sum(te,'all');
for ti=1:ntime
for ne=1:nechoes
functidat = tefuncdat(:,:,:,ti,ne);
functidat = functidat .* te(ne);
functidat = functidat ./ sum_weights;
funcdat(:,:,:,ti) = funcdat(:,:,:,ti)+functidat;
end
end
cmethod = 'BS';
case 2 %tSNR (wi=tSNRi/sum(tSNRi)
mask = MEHB_mask(tefuncdat(:,:,:,:,1));
mask_ind = find(mask>0);
weights = zeros(voldim(1)*voldim(2)*voldim(3),nechoes);
meantei = reshape(mean(tefuncdat,4),[voldim(1)*voldim(2)*voldim(3),nechoes]);
stdtei = reshape(std(tefuncdat,0,4),[voldim(1)*voldim(2)*voldim(3),nechoes]);
weights(mask_ind,:) = meantei(mask_ind,:) ./ stdtei(mask_ind,:);
weights = reshape(weights,[voldim(1),voldim(2),voldim(3),nechoes]);
sum_weights = sum(weights,4);
weights_mask = find(sum_weights>0);
for ti=1:ntime
for ne=1:nechoes
functidat = tefuncdat(:,:,:,ti,ne);
functidat = functidat .* weights(:,:,:,ne);
functidat(weights_mask) = functidat(weights_mask) ./ sum_weights(weights_mask);
funcdat(:,:,:,ti) = funcdat(:,:,:,ti)+functidat;
end
end
cmethod = 'tSNR';
case 3 %tBS (wi=tSNRi * TEi/sum(tSNRi * TEi))
mask = MEHB_mask(tefuncdat(:,:,:,:,1));
mask_ind = find(mask>0);
weights = zeros(voldim(1)*voldim(2)*voldim(3),nechoes);
meantei = reshape(mean(tefuncdat,4),[voldim(1)*voldim(2)*voldim(3),nechoes]);
stdtei = reshape(std(tefuncdat,0,4),[voldim(1)*voldim(2)*voldim(3),nechoes]);
for ie=1:nechoes
weights(mask_ind,ie) = repmat(te(ie),numel(mask_ind),1) .* meantei(mask_ind,ie) ./ stdtei(mask_ind,ie);
end
weights = reshape(weights,[voldim(1),voldim(2),voldim(3),nechoes]);
sum_weights = sum(weights,4);
weights_mask = find(sum_weights>0);
for ti=1:ntime
for ne=1:nechoes
functidat = tefuncdat(:,:,:,ti,ne);
functidat = functidat .* weights(:,:,:,ne);
functidat(weights_mask) = functidat(weights_mask) ./ sum_weights(weights_mask);
funcdat(:,:,:,ti) = funcdat(:,:,:,ti)+functidat;
end
end
cmethod = 'tBS';
case 4 %T2* weigghted (wi(t)=TEi * exp(-TEi/T2*(t)),sum(TEi * exp(-TEi/T2*(t))))
mask = MEHB_mask(tefuncdat(:,:,:,:,1));
mask_ind = find(mask>0);
%based on https://github.com/jsheunis/fMRwhy/tree/master
for ti=1:ntime
tifuncdat = reshape(tefuncdat(:,:,:,ti,:),[voldim(1),voldim(2),voldim(3),nechoes]);
% Create "design matrix" X
X = horzcat(ones(nechoes,1), -te(:));
t2star = zeros(voldim(1)*voldim(2)*voldim(3),1);
Y=[];
for ne=1:nechoes
temptefuncdat = reshape(tifuncdat(:,:,:,ne),[voldim(1)*voldim(2)*voldim(3),1]);
Y=[Y;reshape(temptefuncdat(mask_ind,1),[1,numel(mask_ind)])];
end
Y = max(Y, 1e-11);
% Estimate "beta matrix" by solving set of linear equations
beta_hat = pinv(X) * log(Y);
% Calculate S0 and T2star from beta estimation
T2star_fit = beta_hat(2, :); %is R2*
T2star_thresh_min = 1/1500; % arbitrarily chosen, same as tedana
I_T2star_min = (T2star_fit < T2star_thresh_min); % vector of voxels where T2star value is negative
T2star_fit(I_T2star_min) = 0; % if values inside mask are zero or negative, set them to threshold_min value
t2star(mask_ind) = T2star_fit;
weights = zeros(voldim(1)*voldim(2)*voldim(3),nechoes);
for ne=1:nechoes
weights(:,ne) = repmat(-te(ne),voldim(1)*voldim(2)*voldim(3),1) .* t2star(:,1);
weights(:,ne) = exp(weights(:,ne));
weights(:,ne) = repmat(te(ne),voldim(1)*voldim(2)*voldim(3),1) .* weights(:,ne);
end
weights = reshape(weights,[voldim(1),voldim(2),voldim(3),nechoes]);
sum_weights = sum(weights,4);
weights_mask = find(sum_weights>0);
for ne=1:nechoes
functidat = tefuncdat(:,:,:,ti,ne);
functidat = functidat .* weights(:,:,:,ne);
functidat(weights_mask) = functidat(weights_mask) ./ sum_weights(weights_mask);
funcdat(:,:,:,ti) = funcdat(:,:,:,ti)+functidat;
end
end
cmethod = 'T2* weighted';
case 5 %T2* mapping
mask = MEHB_mask(tefuncdat(:,:,:,:,1));
mask_ind = find(mask>0);
%based on https://github.com/jsheunis/fMRwhy/tree/master
for ti=1:ntime
tifuncdat = reshape(tefuncdat(:,:,:,ti,:),[voldim(1),voldim(2),voldim(3),nechoes]);
% Create "design matrix" X
X = horzcat(ones(nechoes,1), -te(:));
t2star = zeros(voldim(1)*voldim(2)*voldim(3),1);
Y=[];
for ne=1:nechoes
temptefuncdat = reshape(tifuncdat(:,:,:,ne),[voldim(1)*voldim(2)*voldim(3),1]);
Y=[Y;reshape(temptefuncdat(mask_ind,1),[1,numel(mask_ind)])];
end
Y = max(Y, 1e-11);
% Estimate "beta matrix" by solving set of linear equations
beta_hat = pinv(X) * log(Y);
% Calculate S0 and T2star from beta estimation
T2star_fit = beta_hat(2, :); %is R2*
T2star_thresh_min = 1/1500; % arbitrarily chosen, same as tedana
I_T2star_min = (T2star_fit < T2star_thresh_min); % vector of voxels where T2star value is negative
T2star_fit(I_T2star_min) = 0; % if values inside mask are zero or negative, set them to threshold_min value
t2star(mask_ind) = T2star_fit;
zeromask = (t2star>0);
t2star(zeromask) = 1 ./ t2star(zeromask);
funcdat(:,:,:,ti) = reshape(t2star,[voldim(1),voldim(2),voldim(3)]);
end
cmethod = 'T2* mapping';
end
for ti=1:ntime
Vfunc = spm_vol(job.tedat(1).func{ti});
[fpath,fname,~] = fileparts(Vfunc.fname);
nfname = split(fname,'bold_e');
Vout = Vfunc;
Vout.fname = fullfile(fpath,['c' nfname{1} 'bold_' nfname{2} '.nii']);
Vout.descrip = ['combine echoes - ' cmethod];
Vout.pinfo = [1,0,0];
Vout.dt = [spm_type('float32'),spm_platform('bigend')];
Vout.n = [1 1];
Vout = MEHB_write_vol_4d(Vout,funcdat(:,:,:,ti));
out(ti) = {Vout.fname};
end