def process(self, out=None):
if self.tigre_geom.is2D:
data_temp = np.expand_dims(self.get_input().as_array(), axis=1)
arr_out = fdk(data_temp, self.tigre_geom, self.tigre_angles)
arr_out = np.squeeze(arr_out, axis=0)
else:
arr_out = fdk(self.get_input().as_array(), self.tigre_geom,
self.tigre_angles)
if out is None:
out = ImageData(arr_out,
deep_copy=False,
geometry=self.volume_geometry.copy(),
suppress_warning=True)
return out
else:
out.fill(arr_out)
proj,geo, angles = tigreio.NikonDataLoader(datafolder) # as micro-CT datasets are large, optional arguments for loading partial # amount of data is available: # load equidistant angles, but only few: proj, geo, angles = tigreio.NikonDataLoader(datafolder,sampling='equidistant',num_angles=150) # load every X angles (10) proj, geo, angles = tigreio.NikonDataLoader(datafolder,sampling='step',sampling_step=10) # load first X angles (1000) proj, geo, angles = tigreio.NikonDataLoader(datafolder,sampling='continuous',num_angles=1000) # You can directly call reconstruction code now: img=algs.ossart(proj,geo,angles,100) img=algs.fdk(proj,geo,angles) #%% Brucker Skyscan datafolder='~/your_data_path/Bruker/Sample_name/' proj,geo, angles = tigreio.BrukerDataLoader(datafolder) # the same options for sampling and number of angles that exist for Nikon (avobe) exist for Bruker data loaders. # Sometimes a folder will contain more than one dataset. proj,geo, angles = tigreio.BrukerDataLoader(datafolder,dataset_num='all') # load all of them proj,geo, angles = tigreio.BrukerDataLoader(datafolder,dataset_num=0) # load the first #%% DICOM data (only tested on Philips Allura)
default=True)
geo.dDetector = np.array([0.8, 0.8]) * 2 # size of each pixel (mm)
geo.sDetector = geo.dDetector * geo.nDetector
# print(geo)
nangles = 100
angles = np.linspace(0, 2 * np.pi, nangles, endpoint=False, dtype=np.float32)
# Prepare projection data
#head = np.load('src_img_cubic_256.npy')
head = data_loader.load_head_phantom(geo.nVoxel)
proj = tigre.Ax(head, geo, angles, gpuids=gpuids)
# Reconstruct
niter = 20
fdkout = algs.fdk(proj, geo, angles, gpuids=gpuids)
sirtout = algs.ossart(proj, geo, angles, niter, blocksize=20, gpuids=gpuids)
# Measure Quality
# 'RMSE', 'MSSIM', 'SSD', 'UQI'
print('RMSE fdk:')
print(Measure_Quality(fdkout, head, ['nRMSE']))
print('RMSE ossart')
print(Measure_Quality(sirtout, head, ['nRMSE']))
# Plot
fig, axes = plt.subplots(3, 2)
axes[0, 0].set_title('FDK')
axes[0, 0].imshow(fdkout[geo.nVoxel[0] // 2])
axes[1, 0].imshow(fdkout[:, geo.nVoxel[1] // 2, :])
axes[2, 0].imshow(fdkout[:, :, geo.nVoxel[2] // 2])
import sys
from tigre.demos.Test_data import data_loader
from matplotlib import pyplot as plt
from tigre.utilities.Measure_Quality import Measure_Quality
#geo1 = tigre.geometry(mode='cone', high_quality=False, default=True)
geo = tigre.geometry(mode='cone', nVoxel=np.array([256,256,256]),default=True)
geo.dDetector = np.array([0.8, 0.8])*2 # size of each pixel (mm)
geo.sDetector = geo.dDetector * geo.nDetector
niter = 10
nangles = 100
angles = np.linspace(0, 2 * np.pi, nangles, dtype=np.float32)
#head = np.load('src_img_cubic_256.npy') #data_loader.load_head_phantom(geo.nVoxel)
head = data_loader.load_head_phantom(geo.nVoxel)
proj = tigre.Ax(head,geo,angles)
fdkout = algs.fdk(proj,geo,angles)
sirtout = algs.ossart(proj,geo,angles,20,blocksize=20)
# 'RMSE'
# 'MSSIM'
# 'SSD'
# 'UQI'
print('RMSE fdk:')
print(Measure_Quality(fdkout,head,['nRMSE']))
print('RMSE ossart')
print(Measure_Quality(sirtout,head,['nRMSE']))
plt.subplot(211)
plt.imshow(fdkout[geo.nVoxel[0]//2])
plt.subplot(212)
plt.imshow(sirtout[geo.nVoxel[0]//2])
plt.colorbar()
import tigre
import tigre.algorithms as algs
import numpy as np
geo = tigre.geometry(mode='cone',
nVoxel=np.array([32, 64, 128]),
default_geo=True)
from tigre.demos.Test_data import data_loader
img = data_loader.load_head_phantom(geo.nVoxel)
angles = np.linspace(0, np.pi * 2, 100, dtype=np.float32)
proj = tigre.Ax(img, geo, angles)
algs.fdk(proj, geo, angles)
#
# ASD-POCS has a veriety of optional arguments, and some of them are crucial
# to determine the behaviour of the algorithm. The advantage of ASD-POCS is
# the power to create good images from bad data, but it needs a lot of
# tunning.
#
# Optional parameters that are very relevant:
# ----------------------------------------------
# 'maxL2err' Maximum L2 error to accept an image as valid. This
# parameter is crucial for the algorithm, determines at
# what point an image should not be updated further.
# Default is 20% of the FDK L2 norm.
#
# its called epsilon in the paper
epsilon = (
im3DNORM(tigre.Ax(algs.fdk(noise_projections, geo, angles), geo, angles) - noise_projections, 2)
* 0.15
)
# 'alpha': Defines the TV hyperparameter. default is 0.002.
# However the paper mentions 0.2 as good choice
alpha = 0.002
# 'tviter': Defines the amount of TV iterations performed per SART
# iteration. Default is 20
ng = 25
# Other optional parameters
# ----------------------------------------------
# 'lambda': Sets the value of the hyperparameter for the SART iterations.
# Default is 1
def fdk(self, x):
data_temp = np.expand_dims(x.as_array(),axis=1)
arr_out = fdk(data_temp, self.tigre_geom, self.angles)
arr_out = np.squeeze(arr_out, axis=0)
return arr_out
# Coded by: Manasavee Lohvithee
# --------------------------------------------------------------------------
#%%Initialize
import tigre
import numpy as np
from tigre.utilities import sample_loader
from tigre.utilities import CTnoise
import tigre.algorithms as algs
from matplotlib import pyplot as plt
#%% Geometry
geo = tigre.geometry_default(high_resolution=False)
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
# Load thorax phatom data
head = sample_loader.load_head_phantom(geo.nVoxel)
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections,
Poisson=1e5,
Gaussian=np.array([0, 10]))
#%% Some recon, FDK for example
imgFDK = algs.fdk(projections, geo, angles)
# TODO, these are not implemented/accesible in python TIGRE
# Issues #270 #271
# the FDK algorithm has been taken and modified from
# 3D Cone beam CT (CBCT) projection backprojection FDK, iterative reconstruction Matlab examples
# https://www.mathworks.com/matlabcentral/fileexchange/35548-3d-cone-beam-ct--cbct--projection-backprojection-fdk--iterative-reconstruction-matlab-examples
# The algorithm takes, as eny of them, 3 mandatory inputs:
# PROJECTIONS: Projection data
# GEOMETRY : Geometry describing the system
# ANGLES : Propjection angles
# And has a single optional argument:
# FILTER: filter type applied to the projections. Possible options are
# 'ram_lak' (default)
# 'shepp_logan'
# 'cosine'
# 'hamming'
# 'hann'
# The choice of filter will modify the noise and sopme discreatization
# errors, depending on which is chosen.
#
imgFDK1 = algs.fdk(noise_projections, geo, angles, filter="hann")
imgFDK2 = algs.fdk(noise_projections, geo, angles, filter="ram_lak")
# They look quite the same
tigre.plotimg(np.concatenate([imgFDK1, imgFDK2], axis=1), dim="Z")
# but it can be seen that one has bigger errors in the whole image, while
# hte other just in the boundaries
tigre.plotimg(np.concatenate(
[abs(head - imgFDK1), abs(head - imgFDK2)], axis=1),
dim="Z")
#%% Load data and generate projections
# define angles
angles = np.linspace(0, 2 * np.pi, 100)
# Load thorax phatom data
head = sample_loader.load_head_phantom(geo.nVoxel)
# generate projections
projections = tigre.Ax(head, geo, angles)
# add noise
noise_projections = CTnoise.add(projections,
Poisson=1e5,
Gaussian=np.array([0, 10]))
#%% Reconstruct image using OS-SART and FDK
# FDK
imgFDK = algs.fdk(noise_projections, geo, angles)
niter = 50
imgOSSART = algs.ossart(noise_projections, geo, angles, 50)
#%% Lets use PlotProj
#
# plotProj plots the projection data measure on the detector on each angle.
#
# exhaustive list of possible parameters:
# 'Step' : Defines the step size for skippin projections when plotting,
# usefull when there are a big amount of projections. Default is 1
step = 2
# 'Colormap': Defines the colormap used to plot. Default is 'gray'.
