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mri_multi_step_method.py
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import numpy as np
import odl
import functionals as fctls
import algorithms as algs
import operators as ops
import matplotlib.pyplot as plt
from skimage.metrics import structural_similarity as ssim
import deform as defm
import os
import matlab.engine
#%%
# 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
maxIt = 20000 # maximal iterations for black-box registration
save2Disk = True
niter = [10, 20, 50, 100, 200, 500] # number of PALM iterations
data_aligned = False
use_sinfo = True
# Regularization and vector field parameters
alphas = [1e-4, 5e-3]
gamma = 0.9995
eta = 1e-2
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'
# start matlab engine
eng = matlab.engine.start_matlab()
for data_fname in data_fnames:
for sampling in samplings:
folder_out = '{}/{}'.format(output_folder, data_fname)
folder_out += '_sampling_{}'.format(sampling)
if deformation is 'rigid':
folder_out += '_shift_{}-{}_rot_{}'.format(shift[0],shift[1],phi)
elif deformation is 'zoom':
folder_out += '_zoom_{}'.format(zoomfactor)
elif deformation is 'zoom_rigid':
# folder_out += '_shift_{}-{}_rot_{}'.format(shift[0],shift[1],phi)
folder_out += '_zoomr_{}'.format(zoomfactor)
else:
folder_out += '_mixed'
folder_out += '_noise_{}'.format(stddev)
folder_out += '_NonNeg_{}'.format(NonNeg)
folder_out += '_MS_coord'
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(gt * phase))
sinfo = complex_space.real_space.element(sinfo)
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
# 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
im_init = X.zero()
vf_init = Yaff.zero()
# 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, datafit, forward=A)
for it in range(2):
RegParam = alphas[it]
if it == 0:
reg_im = fctls.TV(X, alpha=RegParam, sinfo=None,
NonNeg=NonNeg,
prox_options=prox_options.copy(),
gamma=gamma, eta=eta)
else:
reg_im = fctls.TV(X, alpha=RegParam, sinfo=sinfo,
NonNeg=NonNeg,
prox_options=prox_options.copy(),
gamma=gamma, eta=eta)
g = odl.solvers.SeparableSum(reg_im, reg_affine)
obj = f + g
x_init = Z.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))
ud_vars = [0]
file_out = '{}_{}_da{}_si{}_vars{}_a{:4.2e}_g{:6.4f}_e{:6.4f}'.format('recon',
it, data_aligned, use_sinfo, ud_vars, RegParam, gamma, eta)
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))
clim = [0, 1]
proj = odl.solvers.IndicatorBox(M.range, lower=clim[0], upper=clim[1]).proximal(1) * M
for ni, nid in zip(niter, niter_diff):
recon.run(nid)
if save2Disk is True:
file_out_ni = '{}_{:04d}'.format(file_out, ni)
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))
if it == 0:
#register side information
moving = proj(recon.x[0])
fixed = sinfo
moving_matlab = np.array(moving).tolist()
moving_matlab = matlab.double(moving_matlab)
fixed_matlab = np.array(fixed).tolist()
fixed_matlab = matlab.double(fixed_matlab)
affmap = eng.compute_affine_map(moving_matlab, fixed_matlab, maxIt)
affmap = np.array(affmap)
vf_registered = Yaff.element([affmap[2,1], affmap[2,0],
affmap[0,0]-1, affmap[0,1],
affmap[1,0], affmap[1,1]-1,
])
rel_diff_vf = (vf_registered-vf_gt).norm()/vf_gt.norm()
vf_init = vf_registered
ssim_val = ssim(mask_bdr*M(recon.x[0]),M(gt))
file1 = open("{}.txt".format(folder_out + '/' + file_out_ni),"w")
file1.write("SSIM: {} \n".format(ssim_val))
file1.write("RD Vf: {} \n".format(rel_diff_vf))
file1.close()
eng.quit()