def FISTA(projections, volume, geometry, iterations):
'''
FISTA reconstruction. Right now there is no TV minimization substep here!
'''
global settings
preview = settings['preview']
norm_update = settings['norm_update']
# Sampling:
samp = geometry['proj_sample']
anisotropy = geometry['vol_sample']
shp = numpy.array(projections.shape)
shp //= samp
prj_weight = 1 / (shp[1] * numpy.prod(anisotropy) * max(volume.shape))
# Initialize L2:
settings['norm'] = []
t = 1
volume_t = volume.copy()
volume_old = volume.copy()
print('FISTING in progress...')
sleep(0.5)
# Switch off progress bar if preview is on...
if preview:
ncols = 0
else:
ncols = 50
for ii in tqdm(range(iterations), ncols=ncols):
# Update volume:
if sum(samp) > 3:
proj = projections[::samp[0], ::samp[1], ::samp[2]]
FISTA_step(proj, prj_weight, volume, volume_old, volume_t, t,
geometry)
else:
FISTA_step(projections, volume, volume_old, volume_t, t, geometry)
# Preview
if preview:
display.display_slice(volume, dim=1)
if norm_update:
display.plot(settings['norm'], semilogy=True, title='Resudual norm')
def SIRT(projections, volume, geometry, iterations):
"""
Simultaneous Iterative Reconstruction Technique.
"""
global settings
preview = settings['preview']
norm_update = settings['norm_update']
# Sampling:
samp = geometry['proj_sample']
shp = numpy.array(projections.shape)
shp //= samp
# Initialize L2:
settings['norm'] = []
print('Feeling SIRTy...')
sleep(0.5)
# Switch off progress bar if preview is on...
if preview:
ncols = 0
else:
ncols = 50
# Iterate:
for ii in tqdm(range(iterations), ncols=ncols):
# Update volume:
if sum(samp) > 3:
L2_step(projections[::samp[0], ::samp[1], ::samp[2]], volume,
geometry)
else:
L2_step(projections, volume, geometry)
# Preview
if preview:
display.display_slice(volume, dim=1)
if norm_update:
display.plot(settings['norm'], semilogy=True, title='Resudual L2')
def MULTI_SIRT(projections, volume, geometries, iterations):
"""
A multi-dataset version of SIRT. Here prjections and geometries are lists.
"""
global settings
preview = settings['preview']
norm_update = settings['norm_update']
norm = settings['norm']
# Make sure array is contiguous (if not memmap):
# if not isinstance(projections, numpy.memmap):
# projections = numpy.ascontiguousarray(projections)
# Initialize L2:
norm = []
print('Doing SIRT`y things...')
sleep(0.5)
# Switch off progress bar if preview is on...
if preview:
ncols = 0
else:
ncols = 50
for ii in tqdm(range(iterations), ncols=ncols):
norm = 0
for ii, proj in enumerate(projections):
# This weight is half of the normal weight to make sure convergence is ok:
#prj_weight = 1 / (proj.shape[1] * max(volume.shape))
# Update volume:
L2_step(proj, volume, geometries[ii])
# Preview
if preview:
display.display_slice(volume, dim=1)
if norm_update:
display.plot(norm, semilogy=True, title='Resudual norm')
def EM(projections, volume, geometry, iterations):
"""
Expectation Maximization
"""
global settings
preview = settings['preview']
norm_update = settings['norm_update']
# Make sure that the volume is positive:
if volume.max() <= 0:
volume *= 0
volume += 1
elif volume.min() < 0:
volume[volume < 0] = 0
projections[projections < 0] = 0
# Initialize L2:
settings['norm'] = []
print('Em Emm Emmmm...')
sleep(0.5)
# Switch off progress bar if preview is on...
if preview:
ncols = 0
else:
ncols = 50
# Go!
for ii in tqdm(range(iterations), ncols=ncols):
# Update volume:
EM_step(projections, volume, geometry)
# Preview
if preview:
display.display_slice(volume, dim=1)
if norm_update:
display.plot(settings['norm'], semilogy=True, title='Resudual norm')
import numpy
#%% Create volume and forward project:
# Initialize images:
proj = numpy.zeros([1, 361, 512], dtype = 'float32')
# Define a simple projection geometry:
geometry = io.init_geometry(src2obj = 100, det2obj = 100, det_pixel = 0.01, theta_range = [0, 360], geom_type = 'simple')
print('Volume width is:', 512 * geometry['img_pixel'])
# Create phantom and project into proj:
vol = phantom.abstract_nudes([1, 512, 512], geometry, complexity = 10)
display.display_slice(vol, title = 'Phantom')
# Forward project:
project.forwardproject(proj, vol, geometry)
display.display_slice(proj, title = 'Sinogram')
#%% Unfiltered back-project
# 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')
from flexcalc import spectrum
from flexcalc import process
import numpy
#%% Define a simple projection geometry:
geometry = io.init_geometry(src2obj = 100, det2obj = 100, det_pixel = 0.2,
theta_range = [0, 360], geom_type = 'simple')
#%% Short version of the spectral modeling:
# This is our phantom:
vol = phantom.cuboid([1,128,128], geometry, 8,8,8)
display.display_slice(vol , title = 'Phantom')
# Spectrum of the scanner:
kv = 90
filtr = {'material':'Cu', 'density':8, 'thickness':0.1}
detector = {'material':'Si', 'density':5, 'thickness':1}
E, S = spectrum.effective_spectrum(kv = 90, filtr = filtr, detector = detector)
# Display:
display.plot(E,S, title ='Spectrum')
# Materials list that corresponds to the number of labels in our phantom:
mats = [{'material':'Al', 'density':2.7},]
# Sinogram that will be simultated:
counts = numpy.zeros([1, 128, 128], dtype = 'float32')
path = '/ufs/ciacc/flexbox/al_test/90KV_no_filt/' dark = io.read_tiffs(path, 'di00') flat = io.read_tiffs(path, 'io00') proj = io.read_tiffs(path, 'scan_') 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')
# Compute the spectrum of the system using data @ path: # It is assumed that the data provided has a sample made of a single material (see 'compound' parameter) energy, spectrum = process.data_to_spectrum(path, compound = 'Al', density = 2.7) #%% Read data, reconstruct uncorrected: # Read: proj, meta = process.process_flex(path, sample = 2, skip = 2) # Reconstruct: vol = project.init_volume(proj) project.FDK(proj, vol, meta['geometry']) display.display_slice(vol, title = 'Uncorrected FDK') a,b = process.histogram(vol, rng = [-0.05, 0.15]) #%% Calibrate: proj = process.equivalent_density(proj, meta, energy, spectrum, compound = 'Al', density = 2.7) #%% Reconstruct corrected: vol = project.init_volume(proj) project.FDK(proj, vol, meta['geometry']) display.display_slice(vol, title = 'Corrected FDK')
path = '/ufs/ciacc/flexbox/al_test/90KV_no_filt/'
dark = io.read_tiffs(path, 'di00')
flat = io.read_tiffs(path, 'io00')
proj = io.read_tiffs(path, 'scan_')
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?')
#%% Recon:
vol = numpy.zeros([50, 2000, 2000], dtype='float32')
# Initialize ASTRA geometries:
vol_geom = io.astra_vol_geom(meta['geometry'], vol.shape)
proj_geom = io.astra_proj_geom(meta['geometry'], proj.shape)
# This is ASTRAAA!!!
sin_id = astra.data3d.link('-sino', proj_geom, numpy.ascontiguousarray(proj))
vol_id = astra.data3d.link('-vol', vol_geom, numpy.ascontiguousarray(vol))
cfg = astra.astra_dict('FDK_CUDA')
cfg['ReconstructionDataId'] = vol_id
meta = io.read_meta(path, 'flexray') #%% Prepro: # Now, since the data is on the harddisk, we shouldn't lose the pointer to it! # Be careful which operations to apply. Implicit are OK. proj -= dark proj /= (flat.mean(0) - dark) numpy.log(proj, out=proj) proj *= -1 proj = array.raw2astra(proj) display.display_slice(proj) #%% Recon vol = numpy.zeros([50, 1000, 1000], dtype='float32') # Split the data into 20 subsets: project.settings['block_number'] = 20 project.FDK(proj, vol, meta['geometry']) display.display_slice(vol) #%% SIRT vol = numpy.ones([50, 1000, 1000], dtype='float32')
# Name and range of the parameter to optimize:
key = 'det_rot'
trial_values = numpy.linspace(-0.01, 0.01, 11)
# Subsampling of data (vertical x 10)
samp = [5, 1, 1]
# Optimization:
guess = process.optimize_modifier(trial_values,
proj,
meta['geometry'],
samp=samp,
key=key,
preview=True)
#%% Reconstruct uncorrected:
vol = project.init_volume(proj)
project.FDK(proj, vol, meta['geometry'])
display.display_slice(vol, bounds=[], title='FDK Corrected')
#%% REconstruct corrected:
meta['geometry'][key] = guess
vol = project.init_volume(proj)
project.FDK(proj, vol, meta['geometry'])
display.display_slice(vol, bounds=[], title='FDK Corrected')
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Load a standard CT scan. Use flexCALC.process to preprocess the data. """ #%% Imports from flexcalc import process from flexdata import display from flextomo import project #%% Read data: path = '/ufs/ciacc/flexbox/al_test/90KV_no_filt/' proj, meta = process.process_flex(path, skip=4, sample=4) #%% FDK: vol = project.init_volume(proj) project.FDK(proj, vol, meta['geometry']) display.display_slice(vol, bounds=[], title='FDK')
vol = numpy.zeros([1, h, h], dtype='float32')
proj = numpy.zeros([1, 361, h], dtype='float32')
# Define a simple projection geometry:
src2obj = 100 # mm
det2obj = 100 # mm
det_pixel = 0.001 / x # mm (1 micron)
geometry = io.init_geometry(src2obj, det2obj, det_pixel, [0, 360])
# Create phantom (150 micron wide, 15 micron wall thickness):
vol = phantom.sphere(vol.shape, geometry, 0.08)
vol -= phantom.sphere(vol.shape, geometry, 0.07)
# Show:
display.display_slice(vol, title='Phantom')
# Project:
project.forwardproject(proj, vol, geometry)
#%%
# Get the material refraction index:
c = spectrum.find_nist_name('Calcium Carbonate')
rho = c['density'] / 10
energy = 30 # KeV
n = spectrum.material_refraction(energy, 'CaCO3', rho)
#%% Proper Fresnel propagation for phase-contrast:
# Create Contrast Transfer Functions for phase contrast effect and detector blurring
#%% Read the data path = '/ufs/ciacc/flexbox/JINR_data/projections/SPos0/Energy150000/' proj = io.read_tiffs(path, '00') # Transpose for ASTRA compatibility: proj = array.raw2astra(proj) # Get rid of funny values: proj[(~numpy.isfinite(proj)) | (proj < 0.1)] = 1 proj[proj > 1] = 1 # Da Holy LOG: proj = -numpy.log(proj) display.display_slice(proj, title='Sinogram') display.display_slice(proj, dim=1, title='Projection') #%% Create geometry: # We don't have a dedicated parser for JINR geometry description file. # so we will simply make an ASTRA geometry by hand ''' Mode = cone beam Last angle = 360� Pixel size = 0.055 mm Centre of rotation = 1034.82 Source object distance = 120 mm Source detector distance = 220 mm Direction of rotation = counter-clockwise Vertical centre = 203
