def ADMM_l2Agaussnewton_l1Tfx_tvx(IDEALopt, FTmopt, Tsparse, b, Nite, step, l1_r, tv_r, rho, gn_Nite=3, tvndim=2): Aideal_ftm = opts.joint2operators(IDEALopt, FTmopt) #(FTm,IDEAL)# z = Aideal_ftm.backward(b) #np.zeros(x.shape), z=AH(b) u1 = np.zeros(z.shape, dtype=b.dtype) u2 = np.zeros(z.shape, dtype=b.dtype) u3 = np.zeros(z.shape, dtype=b.dtype) # 2d or 3d, use different proximal funcitons if tvndim is 2: tvprox = pf.prox_tv2d_r elif tvndim is 3: tvprox = pf.prox_tv3d_r # iteration for _ in range(Nite): x1 = pf.prox_l2_gaussnewton(IDEALopt, FTmopt, b, z - u1, rho, gn_Nite) x2 = pf.prox_l1_Tf_soft_thresh(Tsparse.backward, Tsparse.forward, z - u2, l1_r / rho) x3 = tvprox(z - u3, 2.0 * tv_r / rho) z = (x1 + x2 + x3) / 3 + (u1 + u2 + u3) / 3 u1 = u1 + step * (x1 - z) u2 = u2 + step * (x2 - z) u3 = u3 + step * (x3 - z) print('gradient in ADMM %g' % np.linalg.norm(x1 - z)) return x1
def ADMM_l2Aguassnewton_tvx(IDEALopt, FTmopt, b, Nite, step, tv_r, rho, gn_Nite=3, tvndim=2): Aideal_ftm = opts.joint2operators(IDEALopt, FTmopt) #(FTm,IDEAL)# z = Aideal_ftm.backward(b) #np.zeros(x.shape), z=AH(b) u = np.zeros(z.shape, dtype=b.dtype) # 2d or 3d, use different proximal funcitons if tvndim is 2: tvprox = pf.prox_tv2d_r elif tvndim is 3: tvprox = pf.prox_tv3d else: print('dimension imcompatiable in ADMM_l2Afxnb_tvx') return None # iteration for _ in range(Nite): # soft threshold #x = pf.prox_l2_Afxnb_GD(Afunc,invAfunc,b,z-u,rho,20,0.1) #x = pf.prox_l2_Afxnb_CGD( Afunc, invAfunc, b, z-u, rho, gn_Nite ) x = pf.prox_l2_gaussnewton(IDEALopt, FTmopt, b, z - u, rho, gn_Nite) z = tvprox(x + u, 2.0 * tv_r / rho) #pf.prox_tv2d(x+u,2*tv_r/rho) u = u + step * (x - z) print('gradient in ADMM %g' % np.linalg.norm(x - z)) return x
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 ADMM_l2Agaussnewton_l1Tfx(IDEALopt, FTmopt, Tsparse, b, Nite, step, l1_r, rho, gn_Nite=3): Aideal_ftm = opts.joint2operators(IDEALopt, FTmopt) #(FTm,IDEAL)# z = Aideal_ftm.backward(b) #np.zeros(x.shape), z=AH(b) u = np.zeros(z.shape, dtype=b.dtype) # iteration for _ in range(Nite): # soft threshold #x = pf.prox_l2_Afxnb_GD(Afunc,invAfunc,b,z-u,rho,10,0.1) #x = pf.prox_l2_Afxnb_CGD( Afunc, invAfunc, b, z-u, rho, gn_Nite ) x = pf.prox_l2_gaussnewton(IDEALopt, FTmopt, b, z - u, rho, gn_Nite) z = pf.prox_l1_Tf_soft_thresh(Tsparse.backward, Tsparse.forward, x + u, l1_r / rho) u = u + step * (x - z) print('gradient in ADMM %g' % np.linalg.norm(x - z)) return x
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/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_uT2/2016-12-22_7T-volunteer/' # datpath = '/data/larson/brain_uT2/2016-09-13_3T-volunteer/' f = h5py.File(datpath + 'ute_32echo_random-csreconallec_l2_r0p01.mat') #im3d = f['imallplus'][0:10].transpose([1,2,3,0]) #im = im3d[:,40,:,:].squeeze().view(np.complex128) Ndiv = 8 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] 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 #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([1, 1, 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 = 100 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 = 200 #number of iterations #step = 0.1 #step size #th = 0.001 # theshold level #do tv cs mri recon #Nite = 20 #number of iterations #step = 1 #step size #tv_r = 0.01 # regularization term for tv term #rho = 1.0 #ostep = 0.3 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 ) # 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_CGD.mat', { 'xpar': xpar, '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_cg.mat', \ {'xpar':xpar, 'residual':residual}) #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)
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[:, :, :]))