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TomoPy with the ASTRA toolbox

Here is an example on how to use the ASTRA toolbox through its integration with TomoPy as published in [A2].

To reconstruct the image with the ASTRA toolbox instead of TomoPy, change the algorithm keyword to tomopy.astra. Specify which projection kernel to use (proj_type) and which ASTRA algorithm to reconstruct with (method) in the options keyword.

These two cells are an abbreviated setup for Reconstruction with TomoPy.

import dxchange
import matplotlib.pyplot as plt
import tomopy

proj, flat, dark, theta = dxchange.read_aps_32id(
    fname='../../../source/tomopy/data/tooth.h5',
    sino=(0, 2),
)
proj = tomopy.normalize(proj, flat, dark)
rot_center = 296
proj = tomopy.minus_log(proj)

For example, to use a line-based CPU kernel and the FBP method, use:

options = {'proj_type': 'linear', 'method': 'FBP'}
recon = tomopy.recon(proj,
                     theta,
                     center=rot_center,
                     algorithm=tomopy.astra,
                     options=options,
                     ncore=1)
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
plt.imshow(recon[0, :, :])
plt.show()

Reconstructing 1 slice groups with 1 master threads...

If you have a CUDA-capable NVIDIA GPU, reconstruction times can be greatly reduced by using GPU-based algorithms of the ASTRA toolbox, especially for iterative reconstruction methods.

To use the GPU, change the proj_type option to 'cuda', and use CUDA-specific algorithms (e.g. 'FBP_CUDA' for FBP):

options = {'proj_type': 'cuda', 'method': 'FBP_CUDA'}
recon = tomopy.recon(proj,
                     theta,
                     center=rot_center,
                     algorithm=tomopy.astra,
                     options=options,
                     ncore=1)
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
plt.imshow(recon[0, :, :])
plt.show()

Reconstructing 1 slice groups with 1 master threads...

Many algorithms of the ASTRA toolbox support additional options, which can be found in the documentation. These options can be specified using the extra_options keyword.

For example, to use the GPU-based iterative SIRT method with a non-negativity constraint, use:

extra_options = {'MinConstraint': 0}
options = {
    'proj_type': 'cuda',
    'method': 'SIRT_CUDA',
    'num_iter': 200,
    'extra_options': extra_options
}
recon = tomopy.recon(proj,
                     theta,
                     center=rot_center,
                     algorithm=tomopy.astra,
                     options=options,
                     ncore=1)
recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
plt.imshow(recon[0, :, :])
plt.show()

Reconstructing 1 slice groups with 1 master threads...

More information about the projection kernels and algorithms that are supported by the ASTRA toolbox can be found in the documentation: projection kernels and algorithms. Note that only the 2D (i.e. slice-based) algorithms are supported when reconstructing through TomoPy.