-
Notifications
You must be signed in to change notification settings - Fork 1
/
calc_HF_pulse.py
292 lines (239 loc) · 8.91 KB
/
calc_HF_pulse.py
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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
from tools.plot3D import plot3D
from tools.trajectories import gtokTX, ktogTX, spins
from tools.io import readData, combineData, vectorizeData, \
writePulse, readVOPfile, interpData
#from tools.spatialDomainMethod import optimize_RF_grad
from tools.smallTipAngle import smallTipAngle
from tools.bloch import bloch
import numpy as np
import torch
import matplotlib.pyplot as plt
from matplotlib import image
import cv2
#from tools.bloch import bloch
#import tools.bloch as bloch
import os
import time
from scipy.io import loadmat,savemat
start_time = time.time()
#imname = './data/EW4G-63IR.png'
pulsename = "db_TFLb1"
do_plot = True
do_UP = False
if do_UP:
nIter = 7500
else:
nIter = 3500
fname = ['./data/4VRE-G6Q4.mat']
#fname = ['./data/currentData.mat']
if do_UP:
fname = []
fname.append('./data/EBNY-7PGC.mat')
fname.append('./data/ILMH-BG6A.mat')
fname.append('./data/IPPR-4F37.mat')
fname.append('./data/JPY5-SWAO.mat')
fname.append('./data/KYT2-DQX6.mat')
fname.append('./data/M75A-FAX5.mat')
fname.append('./data/EW4G-63IR.mat')
fname.append('./data/IY5C-GQWQ.mat')
fname.append('./data/DJOY-ISET.mat')
vopfile = './data/RFSWDZZMat9001.bin.mat'
downsample=False
Unorm = None
sphericalROI = False
maskName = 'brainMask'
#maskName = 'mask'
#maskName = 'tissueMask'
dev = torch.device('cuda:1')
dtc = torch.complex64
dt = torch.float32
# for circular mask
ctr = [32,32,40]
r = 8
# %% read data
print('reading data')
dat = []
for f in fname:
dat.append(readData(f,dt=dt,dev=dev,Unorm=Unorm,maskName=maskName))
VOPs = readVOPfile(vopfile,dev=dev,dtc=dtc)
# %% preprocess data (fix mask)
for d in dat:
if sphericalROI:
# do spherical ROI
def create_circular_mask(w,h,d, center=None, radius=None):
if center is None: # use the middle of the image
center = (int(w/2), int(h/2), d//2)
if radius is None: # use the smallest distance between the center and image walls
radius = min(center[0], center[1], center[2], w-center[0], h-center[1],d-center[2])
Y, X, Z = np.ogrid[:h, :w, :d]
dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2 + (Z-center[2])**2)
mask = dist_from_center <= radius
return mask
d['mask'][ctr[0]-r-2:ctr[0]+r+2,ctr[1]-r-2:ctr[1]+r+2,ctr[2]-r-2:ctr[2]+r+2] = 0
d['mask'][ctr[0]-r-2:ctr[0]+r+2,ctr[1]-r-2:ctr[1]+r+2,ctr[2]-r-2:ctr[2]+r+2] = torch.tensor(create_circular_mask(2*r+4,2*r+4,2*r+4,radius=r),device=dev)
pass
#dat['mask'][:,:,45:] = 0
print('processing data')
#dat['s'] = dat['s'] #* (223/365)
# combine datasets (UP)
datComb = dat[0];
for i in range(1,len(fname)):
datComb = combineData(datComb,dat[i])
if downsample:
datComb = interpData(datComb,0.5)
dat_v = vectorizeData(datComb)
if False:
# reduce number of Tx channels
dat_v['nTx'] = 2
t = torch.ones([dat_v['nVox'],2],device=dev,dtype=dtc)
t[:,0] = dat_v['s'][:,0::2].sum(1)
t[:,1] = dat_v['s'][:,1::2].sum(1)
dat_v['s'] = t
#%% define target
target = 1.0 * dat_v['mask3D'].clone()
s1 = 35
s2 = 50
if downsample:
s1=int(s1/2)
s2=int(s2/2)
# target[:,:,:s1] = 0
# target[:,:,s1] *= 0.5
# target[:,:,s2] *= 0.5
# target[:,:,s2+1:] = 0
if False:
# do image
image = image.imread(imname).astype(np.float32)
image = image[...,:3].sum(2)
image = torch.from_numpy(image).to(target.device).rot90(1).flip(0)
image /= image.max()
for z in range(target.shape[2]):
target[:,:,z] *= image
if sphericalROI:
# spherical ROI
target = create_circular_mask(target.shape[0],target.shape[1],target.shape[2],center=ctr,radius=r)
target = torch.from_numpy(target).to(dev)
targetFA = 90
if True:
target = target * targetFA
else: # TONE pulse (10..30 deg)
l = torch.linspace(0,20,s2-s1,device=dev,dtype=dt)
target[:,:,s1:s2] = target[:,:,s1:s2] * (l.unsqueeze(0).unsqueeze(0)+10)
if do_plot:
f = plot3D(target.cpu(),pos=[32,32,42])
f.savefig('./img/target.png')
#%% set start value
stepdur=20e-6
nsteps = 100
TR = 1 # TFL
try:
mat = loadmat(f'_{pulsename}.mat')
g_start = torch.from_numpy(mat['g']).to(dev).to(dt)
pulse_start = torch.from_numpy(mat['pulse']).to(dev).to(dtc)
stepdur = mat['stepdur'][0,0]
# fill zeros
pulse_start = pulse_start.masked_fill(pulse_start==0,1e-2)
g_start = g_start.masked_fill(g_start==0,1e-6)
except:
print('couldnt read file,starting with default values')
g_start = None
k = spins(nsteps,kmax=140,alpha=10,beta=0.5,u=8*3,v=5*3,dev=dat_v['s'].device)*0.5
#g_start = ktogTX(k,stepdur)
#g_start = torch.randn(3,nsteps,device=dev,dtype=dt) * 0.0001
#pulse_start = None
pulse_start = torch.ones(nsteps,16,device=dev,dtype=dtc) * targetFA / (nsteps*stepdur) / 2000 * 1.2
# CP mode
phs = torch.linspace(2*torch.pi/dat_v['nTx'],2*torch.pi,dat_v['nTx'],device=dev,dtype=dt)
phs[::2] += -98/180*torch.pi
pulse_start = pulse_start * torch.exp(1j * phs).reshape(1,-1)
torch.autograd.set_detect_anomaly(False)
# system limits (SAR, maxRF, etc)
limits = smallTipAngle.getLimits()
limits['plotscale'] = targetFA * 1.2
limits['targetFA'] = targetFA
limits['maxSAR'] = 9.5
limits['maxRF'] = 170
limits['maxSlew'] = 190 # T/m/s
limits['sliceProfWeight'] = 0
#limits['maxG'] = 1e-12
limits['phaseOpt'] = False
largeFA = (target.max() > 30)
# if FA > 30: design for small FA and up-scale
limits['plotiter'] = 400
limits['dispiter'] = 40
if largeFA:
limits['maxSAR'] = limits['maxSAR']/100
limits['maxRF'] = limits['maxRF']/12
limits['targetFA'] = limits['targetFA']/10
limits['plotscale'] = limits['plotscale']/10
target = target/10
if pulse_start is not None:
pulse_start = pulse_start/10
# first optimize with spatial domain method since it's fast
pulse,g = smallTipAngle.optimize_RF_grad(dat_v,target,niter=nIter,nsteps=nsteps,pulse_start=pulse_start,
g_start=g_start,stepdur=stepdur,VOPs=VOPs,TR=TR,limits=limits,do_plot=do_plot)
if False:
rep = int(stepdur/10e-6);
#gout = torch.zeros(3,rep*g.shape[1])
g = torch.nn.functional.interpolate(g.unsqueeze(1),scale_factor=rep, mode='linear',align_corners=True).squeeze()
pouti = torch.nn.functional.interpolate(pulse.imag.T.unsqueeze(1),scale_factor=rep, mode='linear',align_corners=True).squeeze().T
poutr = torch.nn.functional.interpolate(pulse.real.T.unsqueeze(1),scale_factor=rep, mode='linear',align_corners=True).squeeze().T
pulse = poutr + 1j * pouti
stepdur = stepdur / rep
# then refine with full bloch sim
pulse_start = pulse.clone().detach()
g_start = g.clone().detach()
if largeFA:
target = target * 10
pulse_start *= 10
limits['maxSAR'] = limits['maxSAR']*100
limits['maxRF'] = limits['maxRF']*12
limits['plotscale'] = limits['plotscale']*10
limits['targetFA'] = limits['targetFA'] * 10
if do_UP:
pulse,g = bloch.optimize_RF_grad(dat_v,target,niter=2500,nsteps=nsteps,pulse_start=pulse_start,
g_start=g_start,stepdur=stepdur,VOPs=VOPs,TR=TR,limits=limits)
if largeFA:
pulse,g = bloch.optimize_RF_grad(dat_v,target,niter=1000,nsteps=nsteps,pulse_start=pulse_start,
g_start=g_start,stepdur=stepdur,VOPs=VOPs,TR=TR,limits=limits)
if sphericalROI:
pulse,g = bloch.optimize_RF_grad(dat_v,target,niter=200,nsteps=nsteps,pulse_start=pulse_start,
g_start=g_start,stepdur=stepdur,VOPs=VOPs,TR=TR,limits=limits)
#%%
k = gtokTX(g,stepdur)
#%% export pulse file
mat = dict()
mat['pulse'] = pulse.cpu().numpy()
mat['g'] = g.cpu().numpy()
mat['k'] = k.cpu().numpy()
mat['stepdur'] = stepdur
orientation='unity'
elapsed = time.time() - start_time
print(f"elapsed time: {elapsed:.2f} sec")
print('writing pulse files')
writePulse(f'{pulsename}.ini', pulse.cpu(), g.cpu(),orientation=orientation,stepdur=stepdur,FA=targetFA)
print('writing mat')
savemat(f'{pulsename}.mat',mat)
if True:
print('transferring pulse files')
os.system(f'scp ./{pulsename}.ini root@mars:/opt/medcom/MriCustomer/seq/RFPulses/{pulsename}.ini')
print('file copied successfully')
#%%
SAR = smallTipAngle.calcPulseSAR(pulse, VOPs,TR=TR,stepdur=stepdur)
print(f'SAR = {SAR:6.3f}')
#%%
resvec = torch.cat((g.reshape(-1)/limits['maxG'],
(pulse/limits['maxRF']).real.reshape(-1),
(pulse/limits['maxRF']).imag.reshape(-1)))
#calcErr(dat_v,bestvec,target_v,stepdur,verbose=True,VOPs=VOPs,TR=TR)
#optvec = bestvec.clone().requires_grad_(True)
#spatialDomainMethod.plotVec(bestvec,dat_v,stepdur)
#plotVec(load,dat_v,stepdur)
if do_plot:
f = bloch.plotVec(pulse=pulse,g=g,dat_v=dat_v,stepdur=stepdur,limits=limits,VOPs=VOPs,TR=TR);
filename = f'img/final_{pulsename}.png'
f.savefig(filename)
plt.close()
#torch.save(bestvec,'pulse.dat')
#pulse,g,k = splitVec(bestvec,stepdur)
#pulse_= pulse.reshape(dat_v['nTx'],-1)
#writePulse('./test.ini',pulse_,g,FA=7,stepdur=stepdur)