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Demo 05: Algorithms01

This demo will demonstrate the options for plotting projections and images on TIGRE. The functions have been in previous demos, but in here an exaustive explanation and usage of them is given.

Define Geometry

import tigre
geo=tigre.geometry_default(high_quality=False)

Load data and generate projections

import numpy as np
from tigre.utilities.Ax import Ax
from tigre.demos.Test_data import data_loader
# define angles
angles=np.linspace(0,2*np.pi,dtype=np.float32)
# load head phantom data
head=data_loader.load_head_phantom(number_of_voxels=geo.nVoxel)
# generate projections
projections=Ax(head,geo,angles,'interpolated')

Usage of FDK

import tigre.algorithms as algs
print(help(algs.fdk))

Help on function FDK in module tigre.algorithms.single_pass_algorithms:

FDK(proj, geo, angles, **kwargs)

solves CT image reconstruction.

param proj

np.array(dtype=float32), Data input in the form of 3d

param geo

tigre.utilities.geometry.Geometry Geometry of detector and image (see examples/Demo code)

param angles

np.array(dtype=float32) Angles of projection, shape = (nangles,3) or (nangles,)

param filter

str Type of filter used for backprojection opts: "shep_logan" "cosine" "hamming" "hann"

param verbose

bool Feedback print statements for algorithm progress

param kwargs

dict keyword arguments

return

np.array(dtype=float32)

Usage:

>>> import tigre >>> import tigre.algorithms as algs >>> import numpy >>> from tigre.demos.Test_data import data_loader >>> geo = tigre.geometry(mode='cone',default_geo=True, >>> nVoxel=np.array([64,64,64])) >>> angles = np.linspace(0,2*np.pi,100) >>> src_img = data_loader.load_head_phantom(geo.nVoxel) >>> proj = tigre.Ax(src_img,geo,angles) >>> output = algs.FDK(proj,geo,angles)

tigre.demos.run() to launch ipython notebook file with examples.

--------------------------------------------------------------------This file is part of the TIGRE Toolbox

Copyright (c) 2015, University of Bath and

CERN-European Organization for Nuclear Research All rights reserved.

License: Open Source under BSD.

See the full license at https://github.com/CERN/TIGRE/license.txt

Contact: tigre.toolbox@gmail.com Codes: https://github.com/CERN/TIGRE/

---------------------------------------------------------------------- Coded by: MATLAB (original code): Ander Biguri

PYTHON : Reuben Lindroos

None

imgfdk1=algs.FDK(projections,geo,angles,filter='ram_lak')
imgfdk2=algs.FDK(projections,geo,angles,filter='hann')
# The look quite similar:
tigre.plotimg(np.hstack((imgfdk1,imgfdk2)),slice=32,dim='x')

image

<tigre.utilities.plotimg.plotimg instance at 0x7f740ab0a560>

On the other hand it can be seen that one has bigger errors in the whole image while the other just in the boundaries

dif1=abs(head-imgfdk1)
dif2=abs(head-imgfdk2)
tigre.plotimg(np.hstack((dif1,dif2)),slice=32,dim='x')

image

<tigre.utilities.plotimg.plotimg instance at 0x7f7401a845a8>