# Make volume: vol_rec = numpy.zeros_like(vol) # Backproject: project.settings['block_number'] = 1 project.backproject(proj, vol_rec, geometry) display.display_slice(vol_rec, title = 'Backprojection') #%% Reconstruct # Make volume: vol_rec = numpy.zeros_like(vol) # Use FDK: project.FDK(proj, vol_rec, geometry) display.display_slice(vol_rec, title = 'FDK') #%% Use Expectation Maximization: vol_rec = numpy.zeros_like(vol) project.EM(proj, vol_rec, geometry, iterations = 10) display.display_slice(vol_rec, title = 'EM') #%% SIRT vol = numpy.zeros([1, 512, 512], dtype = 'float32')
phase_ctf *= resolution.get_ctf(proj.shape[::2], 'gaussian',
[det_pixel, sigma * 1])
# Electro-magnetic field image:
proj_i = numpy.exp(-proj * n)
# Field intensity:
proj_i = resolution.apply_ctf(proj_i, phase_ctf)**2
display.display_slice(proj_i, title='Projections (phase contrast)')
#%% Reconstruct with phase contrast:
vol_rec = numpy.zeros_like(vol)
project.FDK(-numpy.log(proj_i), vol_rec, geometry)
display.display_slice(vol_rec, title='FDK')
#%% Invertion of phase contrast based on dual-CTF model:
# Propagator (Dual CTF):
alpha = numpy.imag(n) / numpy.real(n)
dual_ctf = resolution.get_ctf(proj.shape[::2], 'dual_ctf',
[det_pixel, energy, src2obj, det2obj, alpha])
dual_ctf *= resolution.get_ctf(proj.shape[::2], 'gaussian', [det_pixel, sigma])
# Use inverse convolution to solve for blurring and pci
proj_inv = resolution.deapply_ctf(proj_i, dual_ctf, epsilon=0.1)
# Depending on epsilon there is some lof frequency bias introduced...
proj_inv /= proj_inv.max()
meta = io.read_meta(path, 'flexray') #%% Prepro: proj = (proj - dark) / (flat.mean(0) - dark) proj = -numpy.log(proj) proj = array.raw2astra(proj) display.display_slice(proj, title='Sinogram. What else?') #%% FDK Recon vol = project.init_volume(proj) project.FDK(proj, vol, meta['geometry']) display.display_slice(vol, bounds=[], title='FDK') #%% EM vol = numpy.ones([10, 2000, 2000], dtype='float32') project.EM(proj, vol, meta['geometry'], iterations=3) display.display_slice(vol, title='EM') #%% SIRT with additional options vol = numpy.zeros([1, 2000, 2000], dtype='float32')
theta_range = [0, 360]
theta_count = 360
hrz_cntr = proj.shape[2] / 2
vrt_cntr = proj.shape[0] / 2
geometry = io.init_geometry(src2obj,
det2obj,
det_pixel,
theta_range,
geom_type='static_offsets')
# Source position:
geometry['src_hrz'] = (822 - hrz_cntr) * det_pixel
geometry['src_vrt'] = -(203 - vrt_cntr) * det_pixel
# Centre of rotation in mm:
geometry['axs_hrz'] = (1034.82 - hrz_cntr - geometry['src_hrz'] /
det_pixel) * img_pixel + geometry['src_hrz']
# Recon 10 slices:
vol = numpy.zeros([10, 1200, 1200], dtype='float32')
project.FDK(proj, vol, geometry)
display.display_projection(vol, dim=0, bounds=[], title='FDK')
#%% Save geometry:
import os
io.write_astra(os.path.join(path, 'projection.geom'), proj.shape, geometry)
