def test():
# simulated image
mat_contents = sio.loadmat('data/sim_2dmri.mat')
im = mat_contents["sim_2dmri"]
#im = spimg.zoom(xorig, 0.4)
#plotim2(im)
#dwt = opts.DWTnd( wavelet = 'haar', level = 2, axes = (0, 1))
dwt = opts.DWT2d(wavelet='haar', level=4)
nx, ny = im.shape
mask = ut.mask2d(nx, ny, center_r=15)
FTm = opts.FFT2d_kmask(mask)
ut.plotim1(np.absolute(mask)) #plot the mask
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
ut.plotim1(np.absolute(FTm.backward(b))) #undersampled imag
#do soft thresholding
Nite = 100 #number of iterations
step = 1 #step size
th = 1.5 # theshold level
#xopt = solvers.IST_2(FTm.forward, FTm.backward, b, Nite, step,th)
xopt = solvers.IST_3(FTm.forward, FTm.backward, dwt.backward, dwt.forward,
b, Nite, step, th)
ut.plotim1(np.absolute(xopt))
def test():
# simulated image
mat_contents = sio.loadmat('data/kellman_data/PKdata3.mat',
struct_as_record=False,
squeeze_me=True)
xdata = mat_contents["data"]
im = xdata.images
TE = xdata.TE
field = xdata.FieldStrength
fat_freq_arr = 42.58 * field * np.array(
[-3.80, -3.40, -2.60, -1.94, -0.39, 0.60])
fat_rel_amp = np.array([0.087, 0.693, 0.128, 0.004, 0.039, 0.048])
ut.plotim3(np.real(im[:, :, :]))
nx, ny, nte = im.shape
#undersampling
#mask = ut.mask3d( nx, ny, nte, [15,15,0], 0.8)
#FTm = opts.FFT2d_kmask(mask)
FTm = opts.FFT2d()
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
#ut.plotim3(mask)
ut.plotim3(np.absolute(FTm.backward(b))) #undersampled imag
#parameters
xpar = np.zeros((nx, ny, 3), np.complex128)
# IDEAL and FFT jointly
IDEAL = idealc.IDEAL_opt2(TE, 217.0, 1.0) #fat_freq_arr , fat_rel_amp
Aideal_ftm = opts.joint2operators(IDEAL, FTm) #(FTm,IDEAL)#
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
#ut.plotim3(np.absolute(FTm.backward(residual)))
# wavelet and x+d_x
#addx = idealc.x_add_dx()
#addx.set_x(xpar) #should update in each gauss newton iteration
#dwt = opts.DWT2d(wavelet = 'haar', level=4)
#Adwt_addx = opts.joint2operators(dwt, addx)
#do L2 cs mri recon
Nite = 20 #number of iterations
ostep = 1.0
for i in range(20):
dxpar = pf.prox_l2_Afxnb_CGD2(Aideal_ftm.forward, Aideal_ftm.backward,
residual, Nite)
if i % 5 == 0:
ut.plotim3(np.absolute(xpar + ostep * dxpar)[..., 0:2], bar=1)
ut.plotim3(np.real(xpar + ostep * dxpar)[..., 2], bar=1)
ut.plotim3(np.imag(xpar + ostep * dxpar)[..., 2], bar=1)
xpar = xpar + ostep * dxpar #.astype(np.float64)
#xpar[:,:,2] = np.real(xpar[:,:,2])
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
#addx.set_x(xpar) #should update in each gauss newton iteration
ut.plotim3(np.absolute(xpar)[..., 0:2], bar=1)
def test():
# simulated image
mat_contents = sio.loadmat('data/kellman_data/PKdata3.mat',
struct_as_record=False,
squeeze_me=True)
xdata = mat_contents["data"]
im = xdata.images
field = xdata.FieldStrength
b0_gain = 100.0
TE = b0_gain * xdata.TE
fat_freq_arr = (1.0 / b0_gain) * 42.58 * field * np.array(
[-3.80, -3.40, -2.60, -1.94, -0.39, 0.60])
fat_rel_amp = np.array([0.087, 0.693, 0.128, 0.004, 0.039, 0.048])
#ut.plotim3(np.real(im[:,:,:]))
nx, ny, nte = im.shape
#undersampling
mask = ut.mask3d(nx, ny, nte, [15, 15, 0], 0.8)
#FTm = opts.FFT2d_kmask(mask)
FTm = opts.FFTW2d_kmask(mask)
#FTm = opts.FFT2d()
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
#ut.plotim3(mask)
#ut.plotim3(np.absolute(FTm.backward(b))) #undersampled imag
#parameters
xpar = np.zeros((nx, ny, 3), np.complex128)
#xpar[:,:,0] = 10*np.ones((nx,ny))
#ut.plotim3(np.absolute(xpar),[3,-1])
# IDEAL and FFT jointly
IDEAL = idealc.IDEAL_opt2(TE, fat_freq_arr,
fat_rel_amp) #fat_freq_arr , fat_rel_amp
Aideal_ftm = opts.joint2operators(IDEAL, FTm) #(FTm,IDEAL)#
IDEAL.set_x(xpar) #this set the size of data
dwt = opts.DWT2d(wavelet='haar', level=4)
#do tv cs mri recon
Nite = 10 #number of iterations
step = 1 #step size
l1_r = 0.001
tv_r = 0.0001 # regularization term for tv term
rho = 1.0
xpar = ADMM_l2Agaussnewton_l1Tfx(IDEAL, FTm, dwt, b, Nite, step, l1_r, rho)
#xpar = ADMM_l2Aguassnewton_tvx(IDEAL, FTm, b, Nite, step, tv_r, rho )
#xpar = ADMM_l2Agaussnewton_l1Tfx_tvx( IDEAL, FTm, dwt, b, Nite, step, l1_r, tv_r, rho)
ut.plotim3(np.absolute(xpar)[..., 0:2], bar=1)
ut.plotim3(b0_gain * np.real(xpar)[..., 2], bar=1)
ut.plotim3(b0_gain * 2.0 * np.pi * np.imag(xpar)[..., 2], bar=1)
def test():
# simulated image
mat_contents = sio.loadmat('data/brain_32ch.mat');
x = mat_contents["DATA"]
#mask = mat_contents["mask_randm_x3"].astype(np.float)
nx,ny,nc = x.shape
#crop k-space
xcrop = ut.crop2d( x, 16 )
if 0:#do espirit
Vim, sim = espirit_2d(xcrop, x.shape,\
nsingularv = 150, hkwin_shape = (16,16,16), pad_before_espirit = 0, pad_fact = 2 )
#coil map
ut.plotim3(np.absolute(Vim),[4,-1],bar = 1)
ut.plotim1(np.absolute(sim),bar = 1)
#create espirit operator
esp = opts.espirit(Vim)
esp.save('../save_data/espirit_data_2d.mat')
#esp.save('/working/larson/UTE_GRE_shuffling_recon/python_test/save_data/espirit_data_2d.mat')
else:
esp = opts.espirit()
esp.restore('../save_data/espirit_data_2d.mat')
#esp.restore('/working/larson/UTE_GRE_shuffling_recon/python_test/save_data/espirit_data_2d.mat')
#create mask
mask = ut.mask2d( nx, ny, center_r = 15, undersampling = 0.25 )
#FTm = opts.FFT2d_kmask(mask)
FTm = opts.FFTW2d_kmask(mask)
#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.DWT2d(wavelet = 'haar', level=4)
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm,b)
b = b/scaling
ut.plotim1(np.absolute(Aopt.backward(b))) #undersampled imag
#do cs mri recon
Nite = 40 #number of iterations
step = 0.5 #step size
tv_r = 0.002 # regularization term for tv term
rho = 1.0
#th = 1 #threshold
#xopt = solvers.IST_2(FTm.forward,FTm.backward,b, Nite, step,th) #soft thresholding
xopt = solvers.ADMM_l2Afxnb_tvx( Aopt.forward, Aopt.backward, b, Nite, step, tv_r, rho)
#xopt = solvers.ADMM_l2Afxnb_l1x_2( FTm.forward, FTm.backward, b, Nite, step, 100, 1 )
ut.plotim3(np.absolute(xopt))
def test():
ft = opts.FFT2d()
mat_contents = sio.loadmat(
'/working/larson/UTE_GRE_shuffling_recon/20170718_voluteer_ir_fulksp/exp2_ir_fulksp/rawdata.mat'
)
x = mat_contents["da"].squeeze(axis=0).squeeze(axis=3)
mask = mat_contents["mask"].squeeze(axis=0).squeeze(axis=3)
Vim = mat_contents["calib"][40, ...]
#ut.plotim3(np.absolute(x[:,:,:,0]))
im = ft.backward(x)
ut.plotim3(np.absolute(im[:, :, im.shape[2] // 2, :]))
#get shape
nx, ny, nc, nd = x.shape
#create espirit operator
esp = opts.espirit(Vim)
#FTm = opts.FFTnd_kmask(mask)
FTm = opts.FFTW2d_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.DWT2d(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 )
#do wavelet l1 soft thresholding
Nite = 50 #number of iterations
step = 1 #step size
th = 0.1 # theshold level
xopt = solvers.FIST_3(Aopt.forward, Aopt.backward, dwt.backward,
dwt.forward, b, Nite, step, th)
ut.plotim3(np.absolute(xopt[:, :, :]))
def test():
# simulated image
mat_contents = sio.loadmat('data/brain_32ch.mat')
x = mat_contents["DATA"]
#mask = mat_contents["mask_randm_x3"].astype(np.float)
nx, ny, nc = x.shape
#crop k-space
xcrop = ut.crop2d(x, 16)
if 0: #do espirit
Vim, sim = espirit_2d(xcrop, x.shape,\
nsingularv = 150, hkwin_shape = (16,16,16), pad_before_espirit = 0, pad_fact = 2 )
#coil map
ut.plotim3(np.absolute(Vim), [4, -1], bar=1)
ut.plotim1(np.absolute(sim), bar=1)
#create espirit operator
esp = opts.espirit(Vim)
esp.save('../save_data/espirit_data_2d.mat')
else:
esp = opts.espirit()
esp.restore('../save_data/espirit_data_2d.mat')
#create mask
mask = ut.mask2d(nx, ny, center_r=15, undersampling=0.25)
FTm = opts.FFT2d_kmask(mask)
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.DWT2d(wavelet='haar', level=4)
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
ut.plotim1(np.absolute(Aopt.backward(b))) #undersampled imag
#do soft thresholding
Nite = 50 #number of iterations
step = 1 #step size
th = 0.1 # theshold level
#xopt = solvers.IST_2(FTm.forward, FTm.backward, b, Nite, step,th)
xopt = solvers.FIST_3(Aopt.forward, Aopt.backward, dwt.backward,
dwt.forward, b, Nite, step, th)
ut.plotim3(np.absolute(xopt))
def ReconstructADMM_2D(fullysampled_kdata,
mask,
iterations=10,
step=0.05,
tv_r=0.005,
rho=1.0,
is_show=True):
fullysampled_kdata = fullysampled_kdata[..., np.newaxis]
FTm = opts.FFTW2d_kmask(mask)
esp = opts.espirit(sensitivity=np.ones_like(fullysampled_kdata))
Aopt = opts.joint2operators(esp, FTm)
im = FTm.backward(fullysampled_kdata)
dwt = opts.DWT2d(wavelet='haar', level=4)
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
# do cs mri recon
Nite = iterations # number of iterations
step = step # step size
tv_r = tv_r # regularization term for tv term
rho = rho
# th = 1 # threshold
# xopt = solvers.IST_2(FTm.forward,FTm.backward,b, Nite, step,th) #soft thresholding
xopt = solvers.ADMM_l2Afxnb_tvx(Aopt.forward,
Aopt.backward,
b,
Nite,
step,
tv_r,
rho,
is_show=is_show)
# xopt = solvers.ADMM_l2Afxnb_l1x_2( FTm.forward, FTm.backward, b, Nite, step, 100, 1 )
# ut.plotim3(np.absolute(xopt))
return xopt
def test1():
#ft = opts.FFTnd()
#mat_contents = sio.loadmat(pathdat + 'rawdata2.mat');
#x = mat_contents["dataall"][...,0,11:13].astype(np.complex64)#.squeeze(axis = 4)
#Vim = mat_contents["calib"].astype(np.complex64)
#mask = mat_contents["maskn"][...,0,11:13].astype(np.complex64)#.squeeze(axis = 4)
mat_contents = h5py.File(pathdat + 'rawdata.mat')
x = mat_contents['dataall'][:].transpose(
[5, 4, 3, 2, 1,
0]).squeeze(axis=4).view(np.complex128).astype(np.complex64)
Vim = mat_contents["calib"][:].transpose([3, 2, 1, 0]).view(
np.complex128).astype(np.complex64)
mask = mat_contents["maskn"][:].transpose([5, 4, 3, 2, 1,
0]).squeeze(axis=4)
for dd in range(x.shape[-1]):
esp = opts.espirit(Vim)
#FTm = opts.FFTnd_kmask(mask...,dd])
#FTm = opts.FFTWnd_kmask(mask[...,dd], threads = 15)
FTm = cuopts.FFTnd_cuda_kmask(mask[..., dd])
Aopt = opts.joint2operators(esp, FTm)
#wavelet operator
dwt = opts.DWTnd(wavelet='db2', level=4, axes=(0, 1, 2))
# undersampling in k-space
b = x[..., dd]
scaling = ut.optscaling(Aopt, b)
b = b / scaling
#do wavelet l1 soft thresholding
xopt = scaling * solvers.FIST_3(Aopt.forward,
Aopt.backward,
dwt.backward,
dwt.forward,
b,
Nite=25,
step=0.5,
th=0.1)
sio.savemat(pathdat + 'mripy_recon_l1wavelet' + str(dd) + '.mat',
{'xopt': xopt})
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():
# simulated image
mat_contents = sio.loadmat(pathdat, struct_as_record=False, squeeze_me=True)
xdata = mat_contents["data"]
im = xdata.images
field = xdata.FieldStrength
b0_gain = 100.0
TE = b0_gain * xdata.TE
fat_freq_arr = (1.0/b0_gain) * 42.58 * field * np.array([-3.80, -3.40, -2.60, -1.94, -0.39, 0.60])
fat_rel_amp = np.array([0.087, 0.693, 0.128, 0.004, 0.039, 0.048])
ut.plotim3(np.real(im[:,:,:]))
nx,ny,nte = im.shape
#undersampling
mask = ut.mask3d( nx, ny, nte, [15,15,0], 0.8)
#FTm = opts.FFT2d_kmask(mask)
#FTm = opts.FFTW2d_kmask(mask)
FTm = opts.FFT2d()
b = FTm.forward(im)
scaling = ut.optscaling(FTm,b)
b = b/scaling
#ut.plotim3(mask)
ut.plotim3(np.absolute(FTm.backward(b))) #undersampled imag
#parameters
xpar = np.zeros((nx,ny,3), np.complex128)
#xpar[:,:,0] = 10*np.ones((nx,ny))
#ut.plotim3(np.absolute(xpar),[3,-1])
# IDEAL and FFT jointly
IDEAL = idealc.IDEAL_opt2(TE, fat_freq_arr , fat_rel_amp )#fat_freq_arr , fat_rel_amp
Aideal_ftm = opts.joint2operators(IDEAL, FTm)#(FTm,IDEAL)#
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
#ut.plotim3(np.absolute(FTm.backward(residual)))
# wavelet and x+d_x
addx_water = idealc.x_add_dx()
addx_fat = idealc.x_add_dx()
addx_df = idealc.x_add_dx()
#addx = idealc.x_add_dx()
#addx.set_w([1, 1, 0.0001])
addx_water.set_x(xpar[...,0]) #should update in each gauss newton iteration
addx_fat.set_x (xpar[...,1])
addx_df.set_x (xpar[...,2])
dwt = opts.DWT2d(wavelet = 'haar', level=4)
tvop = tvopc.TV2d()
Adwt_addx_w = opts.joint2operators(dwt, addx_water)
Adwt_addx_f = opts.joint2operators(dwt, addx_fat)
Adwt_addx_d = opts.joint2operators(tvop, addx_df)
#Adwt_addx = opts.joint2operators(dwt, addx)
#CGD
Nite = 80
l1_r1 = 0.01
l1_r2 = 0.01
def f(xi):
#return np.linalg.norm(Aideal_ftm.forward(xi)-residual)
return alg.obj_fidelity(Aideal_ftm, xi, residual) \
+ l1_r1 * alg.obj_sparsity(Adwt_addx_w, xi[...,0])\
+ l1_r1 * alg.obj_sparsity(Adwt_addx_f, xi[...,1])\
+ l1_r2 * alg.obj_sparsity(Adwt_addx_d, xi[...,2])
def df(xi):
#return 2*Aideal_ftm.backward(Aideal_ftm.forward(xi)-residual)
gradall = alg.grad_fidelity(Aideal_ftm, xi, residual)
gradall[...,0] += l1_r1 * alg.grad_sparsity(Adwt_addx_w, xi[...,0])
gradall[...,1] += l1_r1 * alg.grad_sparsity(Adwt_addx_f, xi[...,1])
gradall[...,2] += l1_r2 * alg.grad_sparsity(Adwt_addx_d, xi[...,2])
return gradall
#do soft thresholding
#Nite = 20 #number of iterations
#step = 0.1 #step size
#th = 1 # theshold level
#do tv cs mri recon
#Nite = 40 #number of iterations
#step = 1 #step size
#tv_r = 0.01 # regularization term for tv term
#rho = 1.0
ostep = 1.0#0.3
for i in range(20):
#wavelet L1 IST
# dxpar = solvers.IST_3( Aideal_ftm.forward, Aideal_ftm.backward,\
# Adwt_addx.backward, Adwt_addx.forward, residual, Nite, step, th )
#wavelet L1 ADMM
# dxpar = solvers.ADMM_l2Afxnb_l1Tfx( Aideal_ftm.forward, Aideal_ftm.backward, \
# Adwt_addx.backward, Adwt_addx.forward, residual, Nite, step, tv_r, rho,15 )
# TV ADMM
# dxpar = solvers.ADMM_l2Afxnb_tvx( Aideal_ftm.forward, Aideal_ftm.backward, residual\
# , Nite, step, tv_r, rho )
# L2 CGD
# dxpar = pf.prox_l2_Afxnb_CGD2( Aideal_ftm.forward, Aideal_ftm.backward, residual, rho, Nite )
# dxpar = pf.prox_l2_Afxnb_CGD2( Aideal_ftm.forward, Aideal_ftm.backward, residual, Nite )
# L1 CGD
dxpar = alg.conjugate_gradient(f, df, Aideal_ftm.backward(residual), Nite )
ostep,j = alg.BacktrackingLineSearch(f, df, xpar, dxpar)
if i%1 == 0:
ut.plotim3(np.absolute(xpar + ostep*dxpar)[...,0:2],bar=1)
ut.plotim3(np.real(xpar + ostep*dxpar)[...,2],bar=1)
ut.plotim3(np.imag(xpar + ostep*dxpar)[...,2],bar=1)
xpar = xpar + ostep*dxpar#.astype(np.float64)
#if i > 1: #unwrapping on frequence
# xpar[:,:,2] = np.real(unwrap_freq(np.real(xpar[:,:,2])))\
# +1j*(np.imag(xpar[:,:,2]))
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
# addx.set_x(xpar) #should update in each gauss newton iteration
addx_water.set_x(xpar[...,0]) #should update in each gauss newton iteration
addx_fat.set_x (xpar[...,1])
addx_df.set_x (xpar[...,2])
ut.plotim3(np.absolute(xpar)[...,0:2],bar=1)
ut.plotim3(np.real(xpar + ostep*dxpar)[...,2],bar=1)
ut.plotim3(np.imag(xpar + ostep*dxpar)[...,2],bar=1)
sio.savemat(pathdat + 'IDEAL_CGD_result.mat', {'xpar':xpar, 'residual':residual})
def test():
# simulated image
mat_contents = sio.loadmat('data/sim_2dmri.mat')
im = mat_contents["sim_2dmri"]
#im = spimg.zoom(xorig, 0.4)
#plotim2(im)
#dwt = opts.DWTnd( wavelet = 'haar', level = 2, axes = (0, 1))
dwt = opts.DWT2d(wavelet='haar', level=4)
nx, ny = im.shape
mask = ut.mask2d(nx, ny, center_r=15)
FTm = opts.FFT2d_kmask(mask)
#FTm = opts.FFT2d()
#ut.plotim1(np.absolute(mask))#plot the mask
# undersampling in k-space
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
#ut.plotim1(np.absolute(FTm.backward(b))) #undersampled imag
tvop = tvopc.TV2d()
#CGD
Nite = 20
l1_r = 0.01
tv_r = 0.2
#def f(xi):
#ut.plotim1(np.absolute(FTm.backward(FTm.forward(xi)-b)),bar=1)
# return alg.obj_fidelity(FTm, xi, b) \
# + l1_r * alg.obj_sparsity(dwt, xi) \
# + tv_r * alg.obj_sparsity(tvop, xi)
#def df(xi):
#gradall = np.zeros(xi.shape)
# gradall = alg.grad_fidelity(FTm, xi, b)
# gradall += l1_r * alg.grad_sparsity(dwt, xi)
# gradall += tv_r * alg.grad_sparsity(tvop, xi)
# return gradall
def h(xi):
#ut.plotim1(np.absolute(FTm.backward(FTm.forward(xi)-b)),bar=1)
return tv_r * alg.obj_sparsity(tvop, xi) + l1_r * alg.obj_sparsity(
dwt, xi) # +
def dh(xi):
gradall = np.zeros(xi.shape, np.complex128)
gradall += l1_r * alg.grad_sparsity(dwt, xi)
gradall += tv_r * alg.grad_sparsity(tvop, xi)
return gradall
#xopt = alg.conjugate_gradient(f, df, FTm.backward(b), Nite )
#xopt = pf.prox_l2_Afxnb_CGD2( FTm.forward, FTm.backward, b, Nite )
xopt = FTm.backward(b)
for _ in range(100):
xopt = pf.prox_l2_Afxnb_CGD3(FTm.forward, FTm.backward, xopt, b, h, dh,
Nite, 3)
#ut.plotim1(np.absolute(xopt))
#do soft thresholding
#Nite = 100 #number of iterations
#step = 1 #step size
#th = 1.5 # theshold level
#xopt = solvers.IST_2(FTm.forward, FTm.backward, b, Nite, step,th)
#xopt = solvers.IST_3( FTm.forward, FTm.backward, dwt.backward, dwt.forward, b, Nite, step, th )
ut.plotim1(np.absolute(xopt))
def test():
# simulated image
mat_contents = sio.loadmat('data/kellman_data/PKdata3.mat',
struct_as_record=False,
squeeze_me=True)
xdata = mat_contents["data"]
im = xdata.images
field = xdata.FieldStrength
b0_gain = 100.0
TE = b0_gain * xdata.TE
fat_freq_arr = (1.0 / b0_gain) * 42.58 * field * np.array(
[-3.80, -3.40, -2.60, -1.94, -0.39, 0.60])
fat_rel_amp = np.array([0.087, 0.693, 0.128, 0.004, 0.039, 0.048])
ut.plotim3(np.real(im[:, :, :]))
nx, ny, nte = im.shape
#undersampling
mask = ut.mask3d(nx, ny, nte, [15, 15, 0], 0.8)
FTm = opts.FFT2d_kmask(mask)
#FTm = opts.FFTW2d_kmask(mask)
#FTm = opts.FFT2d()
b = FTm.forward(im)
scaling = ut.optscaling(FTm, b)
b = b / scaling
#ut.plotim3(mask)
ut.plotim3(np.absolute(FTm.backward(b))) #undersampled imag
#parameters
xpar = np.zeros((nx, ny, 3), np.complex128)
#xpar[:,:,0] = 10*np.ones((nx,ny))
#ut.plotim3(np.absolute(xpar),[3,-1])
# IDEAL and FFT jointly
IDEAL = idealc.IDEAL_opt2(TE, fat_freq_arr,
fat_rel_amp) #fat_freq_arr , fat_rel_amp
Aideal_ftm = opts.joint2operators(IDEAL, FTm) #(FTm,IDEAL)#
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
#ut.plotim3(np.absolute(FTm.backward(residual)))
# wavelet and x+d_x
addx = idealc.x_add_dx()
addx.set_x(xpar)
#addx.set_w([1, 1, 0.0001])
dwt = opts.DWT2d(wavelet='haar', level=4)
Adwt_addx = opts.joint2operators(dwt, addx)
#do soft thresholding
#Nite = 200 #number of iterations
#step = 0.01 #step size
#th = 0.02 # theshold level
#do tv cs mri recon
Nite = 10 #number of iterations
step = 1 #step size
l1_r = 0.001
tv_r = 0.0001 # regularization term for tv term
rho = 1.0
ostep = 0.3
for i in range(20):
#wavelet L1 IST
# dxpar = solvers.IST_3( Aideal_ftm.forward, Aideal_ftm.backward,\
# Adwt_addx.backward, Adwt_addx.forward, residual, Nite, step, th )
#wavelet L1 ADMM
# dxpar = solvers.ADMM_l2Afxnb_l1Tfx( Aideal_ftm.forward, Aideal_ftm.backward, \
# Adwt_addx.backward, Adwt_addx.forward, residual, Nite, step, l1_r, rho, 200 )
# TV ADMM
# dxpar = solvers.ADMM_l2Afxnb_tvx( Aideal_ftm.forward, Aideal_ftm.backward, residual\
# , Nite, step, tv_r, rho, 15 )
dxpar = solvers.ADMM_l2Afxnb_tvTfx( Aideal_ftm.forward, Aideal_ftm.backward, \
addx.backward, addx.forward, residual, Nite, step, l1_r, rho, 200 )
# L2 CGD
# dxpar = pf.prox_l2_Afxnb_CGD2( Aideal_ftm.forward, Aideal_ftm.backward, residual, rho, Nite )
# dxpar = pf.prox_l2_Afxnb_CGD2( Aideal_ftm.forward, Aideal_ftm.backward, residual, Nite )
if i % 1 == 0:
ut.plotim3(np.absolute(xpar + ostep * dxpar)[..., 0:2], bar=1)
ut.plotim3(b0_gain * np.real(xpar + ostep * dxpar)[..., 2], bar=1)
ut.plotim3(np.imag(xpar + ostep * dxpar)[..., 2], bar=1)
xpar = xpar + ostep * dxpar #.astype(np.float64)
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(b, FTm)
addx.set_x(xpar) #should update in each gauss newton iteration
sio.savemat('data/kellman_data/xpar.mat', {'xpar': xpar})
ut.plotim3(np.absolute(xpar)[..., 0:2], bar=1)
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[:, :, :]))
