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[:, :, :]))