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():
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 test1():
N = 20
k1, k2 = nufftfreqs2d(N, N)
k_c = np.cos(np.multiply(5, k1)) + 1j * np.sin(np.multiply(5, k2)) #
#k_c = np.ones((N,N))#
hwin = hamming2d(N, N)
#ut.plotgray(np.absolute(hwin))
# apply hamming window
k_c = np.multiply(k_c, hwin)
im = dft2d_warp(N, N, k_c)
ut.plotim1(np.absolute(im), bar=True)
#use std fft lib
ft = opts.FFT2d()
npim = ft.backward(k_c)
ut.plotim1(np.absolute(npim), bar=True)
ut.plotim1(np.absolute(im - npim), bar=True)
#interpolatation
im_int = dft2d_warp(5 * N, 5 * N, k_c)
ut.plotim1(np.absolute(im_int), bar=True)
def test():
ft = opts.FFT2d()
# simulated image
mat_contents = sio.loadmat('data/brain_32ch.mat');
x = mat_contents["DATA"]
#ut.plotim1(np.absolute(x[:,:,0]))
im = ft.backward(x[:,:,:])
ut.plotim3(np.absolute(im[:,:,:]))
#crop k-space
xcrop = ut.crop2d( x, 16 )
#do espirit2d
Vim, sim = espirit_2d(xcrop, x.shape,\
nsingularv = 150, hkwin_shape = (16,16,16), pad_before_espirit = 0, pad_fact = 2 )
ut.plotim3(np.absolute(Vim))
esp = opts.espirit(Vim)
esp_im = esp.backward(im)
ut.plotim1(np.absolute(esp_im))
im_recon = esp.forward(esp_im)
ut.plotim3(np.absolute(im_recon[:,:,:]))
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():
ft = opt.FFT2d()
# simulated image
mat_contents = sio.loadmat('data/brain_8ch.mat')
x = mat_contents["DATA"]
#ut.plotim1(np.absolute(x[:,:,0]))
im = ft.backward(x[:, :, :])
ut.plotim3(np.absolute(im[:, :, :]))
#shape of x
nx, ny, nc = x.shape
xcrop = ut.crop2d(x, 30)
#ksp = ft.forward(im)
#ut.plotim1(np.absolute(ksp[:,:]))
#multidimention tensor as the block hankel matrix
h = hk.hankelnd_r(xcrop, (16, 16, 1))
dimh = h.shape
#flatten the tensor to create a matrix
hmtx = h.reshape(
(dimh[0] * dimh[1] * dimh[2], dimh[3], dimh[4], dimh[5])).reshape(
(dimh[0] * dimh[1] * dimh[2], dimh[3] * dimh[4] * dimh[5]))
#svd, could try other approaches
U, s, V = np.linalg.svd(hmtx, full_matrices=False)
#S = np.diag(s)
#ut.plotim1(np.absolute(V[:,0:150]).T)#plot V singular vectors
ut.plot(s) #plot sigular values
#invh = np.zeros(x.shape,complex)
#print h.shape
#hk.invhankelnd(h,invh,(2,3,1))
#reshape vn to generate k-space vn tensor
nsingular = 150 #number of truncated sigular vectors
vn = V[0:nsingular, :].reshape(
(nsingular, dimh[3], dimh[4], dimh[5])).transpose((1, 2, 0, 3))
#zero pad vn, vn matrix of reshaped singular vectors,
#dims of vn: nx,ny,nsingular,ncoil
vn = ut.pad2d(vn, nx, ny)
#plot first singular vecctor Vn[0]
imvn = ft.forward(vn)
#ut.plotim3(np.absolute(imvn[:,:,0,:].squeeze()))#spatial feature of V[:,1] singular vector
sim = 1j * np.zeros((nx, ny))
Vim = 1j * np.zeros((nx, ny, nc))
#Uim = 1j*np.zeros((nx,ny,nc))
for ix in range(nx):
for iy in range(ny):
vpix = imvn[ix, iy, :, :].squeeze()
vpix = np.matrix(vpix).transpose()
vvH = vpix.dot(vpix.getH())
U, s, V = np.linalg.svd(vvH, full_matrices=False)
sim[ix, iy] = s[0]
Vim[ix, iy, :] = V[0, :].squeeze()
#Uim[ix,iy,:] = U[:,0].squeeze()
#plot first eigen vector, eigen value
ut.plotim3(np.absolute(Vim))
#ut.plotim3(np.absolute(Uim))
ut.plotim1(np.absolute(sim))