def test():
ft = opts.FFTnd()
# simulated image
mat_contents = sio.loadmat(
'/working/larson/UTE_GRE_shuffling_recon/brain_mt_recon_20160919/brain_3dMRI_32ch.mat'
)
x = mat_contents["DATA"]
#ut.plotim3(np.absolute(x[:,:,:,0]))
im = ft.backward(x)
ut.plotim3(np.absolute(im[:, :, im.shape[2] // 2, :]))
#crop k-space
xcrop = ut.crop3d(x, 12)
#do espirit2d
Vim, sim = espirit_3d(xcrop, x.shape, 150, hkwin_shape = (12,12,12),\
pad_before_espirit = 0, pad_fact = 2)
esp = opts.espirit(Vim)
esp_im = esp.backward(im)
ut.plotim3(np.absolute(esp_im[:, :, :]))
im_recon = esp.forward(esp_im)
ut.plotim3(np.absolute(im_recon[:, :, im.shape[2] // 2, :]))
def test():
#path = '/home/pcao/3d_recon/'
#matfile = 'Phantom_res256_256_20.mat'
#phantom data
#path = '/working/larson/UTE_GRE_shuffling_recon/UTEcones_recon/20170301/scan_1_phantom/'
#matfile = 'Phantom_utecone.mat'
#lung data
path = '/working/larson/UTE_GRE_shuffling_recon/UTEcones_recon/20170301/lung_exp4_no_prep/'
matfile = 'lung_utecone.mat'
mat_contents = sio.loadmat(path + matfile)
ktraj = mat_contents["ktraj"]
dcf = mat_contents["dcf"]
kdata = mat_contents["kdata"].astype(np.complex64)
ncoils = kdata.shape[3]
#bart nufft assumes the im_shape is weighted on ktraj, so I can extract this info here
im_shape = [
2 * int(np.max(ktraj[0])), 2 * int(np.max(ktraj[1])),
2 * int(np.max(ktraj[2]))
]
# remove the weighting of im_shape from ktraj
ktraj[0, :] = ktraj[0, :] * (1.0 / im_shape[0])
ktraj[1, :] = ktraj[1, :] * (1.0 / im_shape[1])
ktraj[2, :] = ktraj[2, :] * (1.0 / im_shape[2])
#reshape the kdata, flatten the xyz dims
kdata = kdata.reshape((np.prod(kdata.shape[0:3]), ncoils)).squeeze()
#call nufft3d here
nft = cuoptc.NUFFT3d_cuda(im_shape, dcf)
#nft = optc.NUFFT3d(im_shape, dcf)
nft.normalize_set_ktraj(ktraj)
ft = opts.FFTnd()
im = nft.backward(kdata)
x = ft.forward(im)
ut.plotim3(np.absolute(im[:, :, :, 1]), pause_close=5)
#get shape
#nx,ny,nz,nc = x.shape
#crop k-space
xcrop = ut.crop3d(x, 12)
if 0: #do espirit
Vim, sim = espirit_3d(xcrop, x.shape, 500, hkwin_shape = (12,12,12),\
pad_before_espirit = 0, pad_fact = 2, sigv_th = 0.001, nsigv_th = 0.2 )
#coil map
#ut.plotim3(np.absolute(Vim[:,:,im.shape[2]//2,:]),bar = 1)
#ut.plotim3(np.absolute(sim),bar = 1)
#create espirit operator
esp = opts.espirit(Vim)
#esp.save('../save_data/espirit_data_3d.mat')
esp.save(path + 'espirit_data_3d.mat')
else:
esp = opts.espirit()
#esp.restore('../save_data/espirit_data_3d.mat')
esp.restore(path + 'espirit_data_3d.mat')
#ut.plotim1(np.absolute(mask))#plot the mask
Aopt = opts.joint2operators(esp, nft)
#wavelet operator
dwt = opts.DWTnd(wavelet='haar', level=4)
#
scaling = ut.optscaling(Aopt, kdata)
kdata = kdata / scaling
#do tv cs mri recon
#Nite = 20 #number of iterations
#step = 0.5 #step size
#tv_r = 0.002 # regularization term for tv term
#rho = 1.0
#xopt = solvers.ADMM_l2Afxnb_tvx( Aopt.forward, Aopt.backward, kdata, Nite, step, tv_r, rho )
#do wavelet l1 soft thresholding
Nite = 40 #number of iterations
step = 0.1 #step size
th = 0.06 # theshold level
#xopt = solvers.IST_2( Aopt.forward, Aopt.backward, kdata, Nite, step, th )
#xopt = solvers.IST_22( Aopt.forward_backward, Aopt.backward, kdata, Nite, step, th )
#xopt = solvers.FIST_3( Aopt.forward, Aopt.backward, dwt.backward, dwt.forward, kdata, Nite, step, th )
#xopt = solvers.FIST_32( Aopt.forward_backward, Aopt.backward, dwt.backward, dwt.forward, kdata, Nite, step, th )
xopt = solvers.FIST_wrap(Aopt, dwt, kdata, Nite, step, th)
#xopt = solvers.IST_wrap( Aopt, dwt, kdata, Nite, step, th )
ut.plotim3(np.absolute(xopt[:, :, :]), pause_close=5)
sio.savemat(path + 'test_im_th0p06.mat', {'xopt': xopt})
def test():
ft = opts.FFTnd()
mat_contents = sio.loadmat(
'/working/larson/UTE_GRE_shuffling_recon/brain_mt_recon_20160919/brain_3dMRI_32ch.mat'
)
x = mat_contents["DATA"]
#ut.plotim3(np.absolute(x[:,:,:,0]))
im = ft.backward(x)
#ut.plotim3(np.absolute(im[:,:,im.shape[2]//2,:]))
#get shape
nx, ny, nz, nc = x.shape
#crop k-space
xcrop = ut.crop3d(x, 12)
if 1: #do espirit
Vim, sim = espirit_3d(xcrop, x.shape, 150, hkwin_shape = (12,12,12),\
pad_before_espirit = 0, pad_fact = 2)
#coil map
#ut.plotim3(np.absolute(Vim[:,:,im.shape[2]//2,:]),bar = 1)
#ut.plotim3(np.absolute(sim),bar = 1)
#create espirit operator
esp = opts.espirit(Vim)
#esp.save('../save_data/espirit_data_3d.mat')
esp.save(
'/working/larson/UTE_GRE_shuffling_recon/python_test/save_data/espirit_data_3d.mat'
)
else:
esp = opts.espirit()
#esp.restore('../save_data/espirit_data_3d.mat')
esp.restore(
'/working/larson/UTE_GRE_shuffling_recon/python_test/save_data/espirit_data_3d.mat'
)
#create mask
mask = ut.mask3d(nx, ny, nz, [15, 15, 0])
#FTm = opts.FFTnd_kmask(mask)
FTm = opts.FFTWnd_kmask(mask, threads=5)
#ut.plotim1(np.absolute(mask))#plot the mask
Aopt = opts.joint2operators(esp, FTm)
#create image
im = FTm.backward(x)
#ut.plotim3(np.absolute(im[:,:,:]))
#wavelet operator
dwt = opts.DWTnd(wavelet='haar', level=4)
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
#ut.plotim3(np.absolute(Aopt.backward(b))) #undersampled imag
#do tv cs mri recon
Nite = 20 #number of iterations
step = 0.5 #step size
tv_r = 0.002 # regularization term for tv term
rho = 1.0
#xopt = solvers.ADMM_l2Afxnb_tvx( Aopt.forward, Aopt.backward, b, Nite, step, tv_r, rho )
xopt = solvers.ADMM_l2Afxnb_l1Tfx(Aopt.forward, Aopt.backward,
dwt.backward, dwt.forward, b, Nite, step,
tv_r, rho)
#do wavelet l1 soft thresholding
#Nite = 50 #number of iterations
#step = 1 #step size
#th = 0.4 # theshold level
#xopt = solvers.FIST_3( Aopt.forward, Aopt.backward, dwt.backward, dwt.forward, b, Nite, step, th )
ut.plotim3(np.absolute(xopt[:, :, :]))