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/larson/brain_uT2/2016-09-13_3T-volunteer/ute_32echo_random-csreconallec_l2_r0p01.mat', struct_as_record=False, squeeze_me=True)
#datpath = '/data/larson/brain_uT2/2016-12-19_7T-volunteer/' #
datpath = '/data/larson/brain_uT2/2016-09-13_3T-volunteer/'
f = h5py.File(datpath + 'ute_32echo_random-csreconallec_l2_r0p01.mat')
#datpath = '/data/larson/brain_uT2/2017-11-17_3T-DTI-volunteer/'
#f = h5py.File(datpath+'P31232_ir4echo.7-csreconallec_l2_r0p01.mat')
#im3d = f['imallplus'][0:10].transpose([1,2,3,0])
#im = im3d[:,40,:,:].squeeze().view(np.complex128)
Ndiv = 4
im3d = f['imallplus'][0:Ndiv].transpose([1, 3, 2, 0])
im = im3d[35, :, :, :].squeeze().view(np.complex128)
b0_gain = 1000.0
TE = b0_gain * 1e-6 * f['TE'][0][0:Ndiv]
#TE = TEi[0:Ndiv]
field = 3.0
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])
print(1000 / b0_gain * TE)
#ut.plotim3(np.absolute(im[:,:,-10:-1]),[4,-1])
nx, ny, nte = im.shape
if 0:
nte = nte - 1
im = 1j * np.zeros((nx, ny, Ndiv))
for nd in range(Ndiv):
im[:, :, nd] = (imi[:, :, nd] - imi[:, :, nd + 1]) / (TEi[nd] -
TEi[nd + 1])
print(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.scaling(im)
im = im / scaling
ut.plotim3(np.absolute(im[:, :, :]), [4, -1], bar=1, pause_close=2)
#ut.plotim3(mask)
#ut.plotim3(np.absolute(FTm.backward(b))) #undersampled imag
#parameters
xpar = np.zeros((nx, ny, 4), np.complex128)
#xpar[:,:,0] = 10*np.ones((nx,ny))
#ut.plotim3(np.absolute(xpar),[3,-1])
# IDEAL and FFT jointly
IDEAL = idealc.IDEAL_fatmyelin_opt2(
TE, fat_freq_arr, fat_rel_amp) #fat_freq_arr , fat_rel_amp
Aideal_ftm = IDEAL #opts.joint2operators(IDEAL, FTm)#(FTm,IDEAL)#
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(im)
#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_dfwater = idealc.x_add_dx()
addx_dffat = idealc.x_add_dx()
addx = idealc.x_add_dx()
addx.set_x(xpar)
#addx.set_w([0.01, 0.01, 0.0001])
#addx_water.set_x (xpar[...,0]) #should update in each gauss newton iteration
#addx_fat.set_x (xpar[...,1])
#addx_dfwater.set_x (xpar[...,2])
#addx_dffat.set_x (xpar[...,3])
dwt = opts.DWT2d(wavelet='haar', level=4)
tvop = tvopc.TV2d_r()
#Adwt_addx_w = opts.joint2operators(tvop, addx_water)
#Adwt_addx_f = opts.joint2operators(tvop, addx_fat)
#Adwt_addx_dwat = opts.joint2operators(tvop, addx_dfwater)
#Adwt_addx_dfat = opts.joint2operators(tvop, addx_dffat)
Adwt_addx = opts.joint2operators(dwt, addx)
#CGD
#Nite = 400
#l1_r1 = 0.01
#l1_r2 = 0.01
#l1_r3 = 0.01
#l1_r4 = 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_r2 * alg.obj_sparsity(Adwt_addx_f, xi[...,1])\
# + l1_r3 * alg.obj_sparsity(Adwt_addx_dwat, xi[...,2])\
# + l1_r4 * alg.obj_sparsity(Adwt_addx_dfat, xi[...,3])
#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_r2 * alg.grad_sparsity(Adwt_addx_f, xi[...,1])
# gradall[...,2] += l1_r3 * alg.grad_sparsity(Adwt_addx_dwat, xi[...,2])
# gradall[...,3] += l1_r4 * alg.grad_sparsity(Adwt_addx_dfat, xi[...,3])
# return gradall
#do soft thresholding
Nite = 40 #number of iterations
step = 0.001 #step size
th = 0.001 # theshold level
#do tv cs mri recon
#Nite = 20 #number of iterations
#step = 1 #step size
#tv_r = 0.001 # regularization term for tv term
#rho = 1.0
ostep = 0.6
for i in range(40):
#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,25 )
# TV ADMM
# dxpar = solvers.ADMM_l2Afxnb_tvx( Aideal_ftm.forward, Aideal_ftm.backward, residual\
# , Nite, step, tv_r, rho )
# dxpar = solvers.ADMM_l2Afxnb_tvTfx( Aideal_ftm.forward, Aideal_ftm.backward, \
# addx.backward, addx.forward, residual, Nite, step, tv_r, rho,25)
# 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 = pf.prox_l2_Afxnb_CGD2( IDEAL.forward, IDEAL.backward, residual, Nite )
# dxpar = alg.conjugate_gradient(f, df, Aideal_ftm.backward(residual), Nite )
# ostep,j = alg.BacktrackingLineSearch(f, df, xpar, dxpar)
if i % 1 == 0:
nxpar = xpar + ostep * dxpar
nxpar[..., 1] = 10 * nxpar[..., 1]
ut.plotim3(np.absolute(nxpar)[..., 0:2],
colormap='viridis',
bar=1,
vmin=0,
vmax=1,
pause_close=2)
ut.plotim3(b0_gain * np.real(nxpar)[..., 2],
colormap='viridis',
bar=1,
pause_close=2)
ut.plotim3(b0_gain * np.imag(nxpar)[..., 2],
colormap='viridis',
bar=1,
pause_close=2)
ut.plotim3(b0_gain * np.real(nxpar)[..., 3],
colormap='viridis',
bar=1,
pause_close=2)
ut.plotim3(b0_gain * np.imag(nxpar)[..., 3],
colormap='viridis',
bar=1,
pause_close=2)
#sio.savemat(datpath + 'cs_ideal_fitting/cs_IDEAL_ADMM_dyn8.mat', {'xpar': nxpar, 'residual': residual})
xpar = xpar + ostep * dxpar #.astype(np.float64)
if i > 1: #fix the frequence offset to be equal for two components
freq_ave = 0.5 * np.real(xpar[:, :, 2]) + 0.5 * np.real(xpar[:, :,
3])
xpar[:, :, 2] = freq_ave + 1j * (np.imag(xpar[:, :, 2]))
xpar[:, :, 3] = freq_ave + 1j * (np.imag(xpar[:, :, 3]))
IDEAL.set_x(xpar) #should update in each gauss newton iteration
residual = IDEAL.residual(im)
ut.plotim3(np.absolute(residual), [4, -1], bar=1, pause_close=2)
sio.savemat('../save_data/myelin/ideal_result.mat', \
{'xpar':xpar})
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_dfwater.set_x(xpar[...,2])
#addx_dffat.set_x (xpar[...,3])
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)
ut.plotim3(np.real(xpar + ostep * dxpar)[..., 3], bar=1)
ut.plotim3(np.imag(xpar + ostep * dxpar)[..., 3], bar=1)