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mri_affreg_multigrid.py
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mri_affreg_multigrid.py
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import numpy as np
import odl
import functionals as fctls
import algorithms as algs
import operators as ops
import deform as defm
import matplotlib.pyplot as plt
import skimage.transform as sktransform
from skimage.metrics import structural_similarity as ssim
import os
#%%
# Model parameters
stddev = 0.001 # standard deviation for gaussian noise
phi = 0.1
shift = [-0.02, -0.08]
zoomfactor = 0.85
deformation = 'zoom_rigid' #'zoom', 'rigid', 'mixed'
NonNeg = False
unitary = False
# Algorithmic parameters
save2Disk = True
niter = [10, 20, 50, 100, 500] # number of PALM iterations per resolution
resolutions = [32,64,128,256]
data_aligned = False
# Vectorfield parameters
gamma = 0.9995
eta = 1e-2
# Define reconstruction settings
settings = ['tv_recon', 'dtv_affreg_recon']
data_folder = '../data'
data_fnames = ['mri_patienta_ch0'] #['mri_brainweba_ch0', 'mri_phantom_ch0']
samplings = ['radialbl-15'] #['cartesian-2', 'cartesian-4','random-0.5','radial-70','radialbl-30',]
output_folder = '../results'
for data_fname in data_fnames:
for sampling in samplings:
for setting in settings:
if setting == 'tv_recon':
use_sinfo = False
ud_vars = [0]
alpha = 5e-4
elif setting == 'dtv_affreg_recon':
use_sinfo = True
ud_vars = [0,1]
alpha = 5e-3
else:
raise ValueError('Unknown setting')
folder_out = '{}/{}'.format(output_folder, data_fname)
folder_out += '_sampling_{}'.format(sampling)
if deformation == 'rigid':
folder_out += '_shift_{}-{}_rot_{}'.format(shift[0],shift[1],phi)
elif deformation == 'zoom':
folder_out += '_zoom_{}'.format(zoomfactor)
elif deformation == 'zoom_rigid':
folder_out += '_zoomr_{}'.format(zoomfactor)
else:
folder_out += '_mixed'
folder_out += '_noise_{}'.format(stddev)
folder_out += '_multigrid'
if not os.path.exists(folder_out):
os.makedirs(folder_out)
# Target space
gt, sinfo = np.load('{}/{}.npy'.format(data_folder, data_fname))
simage = sinfo.shape
complex_space = odl.uniform_discr([-1, -1], [1, 1], simage,
dtype='complex', interp='linear')
X = complex_space.real_space ** 2
V = X[0].tangent_bundle
Yaff = odl.tensor_space(6)
Z = odl.ProductSpace(X, Yaff)
J = ops.Complex2Real(complex_space)
M = ops.Magnitude(X)
# Ground truth image
phase = complex_space.element(
lambda x: np.exp(1j * .8 * (np.sin(3 * x[0] ** 2) +
np.cos(5 * x[1] ** 3))))
gt = J(complex_space.element(sktransform.resize(gt, simage)) * phase)
sinfo = complex_space.real_space.element(sktransform.resize(sinfo, simage))
mask_bdr = np.zeros(sinfo.shape)
mask_bdr[25:-24,25:-24] = 1
gt_bdr = gt*mask_bdr
gt = gt_bdr
sinfo_bdr = sinfo*mask_bdr
sinfo = sinfo_bdr
# Generate affine map and data image
if deformation is 'rigid':
cosp = np.cos(phi)
sinp = np.sin(phi)
disp_func = [
lambda x: (cosp-1)*x[0] - sinp*x[1] + shift[0],
lambda x: sinp*x[0] + (cosp-1)*x[1] + shift[1]]
vf_gt = Yaff.element([shift[0], shift[1],
cosp-1, -sinp,
sinp, cosp-1])
elif deformation is 'zoom':
disp_func = [
lambda x: (zoomfactor-1)*x[0] ,
lambda x: (zoomfactor-1)*x[1] ]
vf_gt = Yaff.element([0, 0,
zoomfactor*-1, 0,
0, zoomfactor-1])
elif deformation is 'mixed':
sinp = np.sin(phi)
disp_func = [
lambda x: (zoomfactor-1)*x[0] + 0.04*x[1] + shift[0],
lambda x: sinp*x[0] + (zoomfactor-1)*x[1] + shift[1]]
vf_gt = Yaff.element([shift[0], shift[1],
zoomfactor-1, 0.04,
sinp, zoomfactor-1])
elif deformation is 'zoom_rigid':
cosp = np.cos(phi)
sinp = np.sin(phi)
disp_func = [
lambda x: (zoomfactor*cosp-1)*x[0] - zoomfactor*sinp*x[1] + shift[0],
lambda x: zoomfactor*sinp*x[0] + (zoomfactor*cosp-1)*x[1] + shift[1]]
vf_gt = Yaff.element([shift[0], shift[1],
zoomfactor*cosp-1, -zoomfactor*sinp,
zoomfactor*sinp, zoomfactor*cosp-1])
vf = V.element(disp_func)
X_plot = odl.uniform_discr([-1, -1], [1, 1], (15, 15), interp='linear')
V_plot = X_plot.tangent_bundle
x,y = X_plot.meshgrid
u = disp_func[0]([x,y])
v = disp_func[1]([x,y])
plt.quiver(x,y,u,v,color='yellow')
plt.axis('equal'),
plt.axis([-1.3,1.3,-1.3,1.3]),
# plt.axis('off'),
plt.gca().set_aspect('equal', adjustable='box'),
plt.savefig(folder_out + '/deformation_field.png',
bbox_inches='tight',
pad_inches=0,
transparent=True, dpi=600,format='png')
deform_op = defm.LinDeformFixedDisp(vf)
deformed_gt = odl.DiagonalOperator(deform_op, deform_op)(gt)
# Forward operator
if unitary is True:
F = ops.UnitaryRealFourierTransform(X)
else:
F = ops.RealFourierTransform(X)
sampling_parts = sampling.split('-')
if sampling_parts[0] == 'cartesian':
r = int(sampling_parts[1])
sampling_op, mask = ops.get_cartesian_sampling(F.range[0], r)
elif sampling_parts[0] == 'random':
p = float(sampling_parts[1])
sampling_op, mask = ops.get_random_sampling(F.range[0], p)
elif sampling_parts[0] == 'radial':
num_angles = int(sampling_parts[1])
sampling_op, mask = ops.get_radial_sampling(F.range[0], num_angles)
elif sampling_parts[0] == 'radialbl':
num_angles = int(sampling_parts[1])
sampling_op, mask = ops.get_radial_sampling(F.range[0], num_angles, block=10)
S = odl.DiagonalOperator(sampling_op, 2)
A = S * F
# data
noise = odl.phantom.white_noise(A.range, mean=0, stddev=stddev, seed=1)
aligned_data = A(gt) + noise
deformed_data = A(deformed_gt) + noise
file1 = open("{}.txt".format(folder_out + '/' + 'PsInv'),"w")
ssim_val_psinv = ssim(M(F.inverse(S.adjoint(aligned_data))),M(gt))
file1.write("SSIM: {} \n".format(ssim_val_psinv))
file1.close()
# Save data images
plt.imsave(folder_out + '/' + data_fname + '_sampling_pattern.png',
mask, cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_data.png',
np.fft.fftshift(M(F(gt))), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_data_deformed.png',
np.fft.fftshift(M(F(deformed_gt))), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_ift_deformed.png',
M(F.inverse(S.adjoint(deformed_data))), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_ift.png',
M(F.inverse(S.adjoint(aligned_data))), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_gt_deformed.png',
M(deformed_gt), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_gt.png',
M(gt), cmap='gray')
plt.imsave(folder_out + '/' + data_fname + '_sinfo.png',
sinfo, cmap='gray')
#%% reconstruction
complex_space_first = odl.uniform_discr([-1, -1], [1, 1],
(resolutions[0],resolutions[0]),
dtype='complex', interp='linear')
X_first = complex_space_first.real_space ** 2
im_init = X_first.zero()
vf_init = Yaff.zero()
ind = 0
alphas = [alpha*5**i for i in range(len(resolutions))]
alphas.reverse()
for res in resolutions:
RegParam = alphas[ind]
simage_res = (res,res)
if use_sinfo is True:
sinfo_res = sktransform.resize(sinfo, simage_res)
else:
sinfo_res = None
complex_space_res = odl.uniform_discr([-1, -1], [1, 1],
simage_res, dtype='complex',
interp='linear')
X_res = complex_space_res.real_space ** 2
V_res = X_res[0].tangent_bundle
Z_res = odl.ProductSpace(X_res, Yaff)
# Forward operator
upsampling = odl.DiagonalOperator(ops.Subsampling(X[0], X_res[0], 0).adjoint, 2)
A_res = S * F * upsampling
# Set some parameters and the general TV prox options
prox_options = {}
prox_options['name'] = 'FGP'
prox_options['warmstart'] = True
prox_options['p'] = None
prox_options['tol'] = None
prox_options['niter'] = 10 #100
strong_convexity = 0
# Define non-smooth part
reg_affine = odl.solvers.ZeroFunctional(Yaff)
step = 10
if data_aligned is True:
datafit = 0.5 * odl.solvers.functional.L2NormSquared(aligned_data.space).translated(aligned_data)
else:
datafit = 0.5 * odl.solvers.functional.L2NormSquared(deformed_data.space).translated(deformed_data)
f = fctls.DataFitDisp(Z_res, datafit, forward=A_res)
file_out = 'recon_da{}_si{}_vars{}_a{:4.2e}_g{:6.4f}_e{:6.4f}_res{}'.format(
data_aligned, use_sinfo, ud_vars, RegParam, gamma, eta, res)
reg_im = fctls.TV(X_res, alpha=RegParam, sinfo=sinfo_res,
NonNeg=NonNeg,
prox_options=prox_options.copy(),
gamma=gamma, eta=eta)
g = odl.solvers.SeparableSum(reg_im, reg_affine)
# Define objective functional
obj = f + g
x_init = Z_res.element([im_init,vf_init])
cb = (odl.solvers.CallbackPrintIteration(end=', ') &
odl.solvers.CallbackPrintTiming(cumulative=False, end=', ') &
odl.solvers.CallbackPrintTiming(fmt='total={:.3f} s', cumulative=True) &
odl.solvers.CallbackPrint(step=step, func=obj, fmt='obj={!r}') &
odl.solvers.CallbackShow(step=step))
recon = algs.PALM(f, g, ud_vars=ud_vars.copy(), x=x_init, niter=None,
callback=cb, L=None, tol=1e-9)
niter_diff = [niter[0]] + list(np.diff(niter))
M_res = ops.Magnitude(X_res)
clim = [0, 1]
proj = odl.solvers.IndicatorBox(M_res.range, lower=clim[0], upper=clim[1]).proximal(1) * M_res
for ni, nid in zip(niter, niter_diff):
recon.run(nid)
if 1 in ud_vars:
vf = ops.Embedding_Affine(Yaff, V_plot)(recon.x[1])
plt.quiver(x, y, vf[0], vf[1], color='red'),
plt.axis('equal'),
plt.axis([-1.3,1.3,-1.3,1.3]),
plt.gca().set_aspect('equal', adjustable='box'),
if save2Disk is True:
file_out_ni = '{}_{:04d}'.format(file_out, ni)
if 1 in ud_vars:
file_out_def_ni = '{}_def_{:04d}'.format(file_out, ni)
plt.savefig(folder_out + '/' + file_out_def_ni + '.png',
bbox_inches='tight',
pad_inches=0,
transparent=True,
dpi=600, format='png')
plt.imsave(folder_out + '/' + file_out_ni + '.png',
proj(recon.x[0]), cmap='gray')
np.save('{}.npy'.format(folder_out + '/' + file_out_ni), list(recon.x))
rel_diff_vf = (recon.x[1]-vf_gt).norm()/vf_gt.norm()
file1 = open("{}.txt".format(folder_out + '/' + file_out_ni + '_def'),"w")
file1.write("RelDiff VF: {} \n".format(rel_diff_vf))
file1.close()
complex_space_new = odl.uniform_discr([-1, -1], [1, 1],
(2*res,2*res), dtype='complex',
interp='linear')
X_new = complex_space_new.real_space ** 2
sampling = odl.DiagonalOperator(ops.Subsampling(X_new[0], X_res[0], 0), 2)
im_init = sampling.adjoint(recon.x[0]).copy()
vf_init = recon.x[1].copy()
ind += 1
ssim_val = ssim(mask_bdr*M(recon.x[0]),M(gt))
rel_diff_vf = (recon.x[1]-vf_gt).norm()/vf_gt.norm()
file1 = open("{}.txt".format(folder_out + '/' + file_out_ni),"w")
file1.write("SSIM: {} \n".format(ssim_val))
if use_sinfo is False:
ssim_val_def = ssim(M(recon.x[0]),M(deformed_gt))
file1.write("SSIM def: {} \n".format(ssim_val_def))
file1.write("RelDiff VF: {} \n".format(rel_diff_vf))
file1.close()