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Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox

Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox
Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox

3D tomographic imaging requires the computation of solutions to very large inverse problems. In many applications, iterative algorithms provide superior results, however, memory limits in available computing hardware restrict the size of problems that can be solved. For this reason, iterative methods are not normally used to reconstruct typical data sets acquired with lab based CT systems. We thus use state of the art techniques such as dual buffering to develop an efficient strategy to compute the required operations for iterative reconstruction. This allows the iterative reconstruction of volumetric images of arbitrary size using any number of GPUs, each with arbitrarily small memory. Strategies for both the forward and backprojection operators are presented, along with two regularization approaches that are easily generalized to other projection types or regularizers. The proposed improvement also accelerates reconstruction of smaller images on single or multiple GPU systems, providing faster code for time-critical applications. The resulting algorithm has been added to the TIGRE toolbox, a repository for iterative reconstruction algorithms for general CT, but this memory-saving and problem-splitting strategy can be easily adapted for use with other GPU-based tomographic reconstruction code.

Computed Tomography, Iterative reconstruction, Software, multi-GPU
0743-7315
52-63
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Lindroos, Reuben J.
d448b02f-b678-4cf8-ae23-5c7efb0b30e0
Bryll, Robert
8b88a342-0c45-429f-bb29-1e7c6952b1e0
Towsyfyan, Hossein
f1f4fa2a-20e4-4519-a66b-2faecb50173d
Deyhle, Hans
aba9cd34-97a0-4238-8255-af673e3beb1a
Harrane, Ibrahim
a0914323-e4a4-4b1f-a0fd-e2c4c69c06bd
Boardman, Richard
5818d677-5732-4e8a-a342-7164dbb10df1
Mavrogordato, Mark
faedf03d-e357-4ec3-818e-e5ff5368fdf0
Dosanjh, Manjit
1928d51e-2162-4c80-91d4-e177f1a9fcd9
Hancock, Steven
62fdd2f6-166f-4a3d-901e-0814f3d9cf9c
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Lindroos, Reuben J.
d448b02f-b678-4cf8-ae23-5c7efb0b30e0
Bryll, Robert
8b88a342-0c45-429f-bb29-1e7c6952b1e0
Towsyfyan, Hossein
f1f4fa2a-20e4-4519-a66b-2faecb50173d
Deyhle, Hans
aba9cd34-97a0-4238-8255-af673e3beb1a
Harrane, Ibrahim
a0914323-e4a4-4b1f-a0fd-e2c4c69c06bd
Boardman, Richard
5818d677-5732-4e8a-a342-7164dbb10df1
Mavrogordato, Mark
faedf03d-e357-4ec3-818e-e5ff5368fdf0
Dosanjh, Manjit
1928d51e-2162-4c80-91d4-e177f1a9fcd9
Hancock, Steven
62fdd2f6-166f-4a3d-901e-0814f3d9cf9c
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead

Biguri, Ander, Lindroos, Reuben J., Bryll, Robert, Towsyfyan, Hossein, Deyhle, Hans, Harrane, Ibrahim, Boardman, Richard, Mavrogordato, Mark, Dosanjh, Manjit, Hancock, Steven and Blumensath, Thomas (2020) Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox. Journal of Parallel and Distributed Computing, 146, 52-63. (doi:10.1016/j.jpdc.2020.07.004).

Record type: Article

Abstract

3D tomographic imaging requires the computation of solutions to very large inverse problems. In many applications, iterative algorithms provide superior results, however, memory limits in available computing hardware restrict the size of problems that can be solved. For this reason, iterative methods are not normally used to reconstruct typical data sets acquired with lab based CT systems. We thus use state of the art techniques such as dual buffering to develop an efficient strategy to compute the required operations for iterative reconstruction. This allows the iterative reconstruction of volumetric images of arbitrary size using any number of GPUs, each with arbitrarily small memory. Strategies for both the forward and backprojection operators are presented, along with two regularization approaches that are easily generalized to other projection types or regularizers. The proposed improvement also accelerates reconstruction of smaller images on single or multiple GPU systems, providing faster code for time-critical applications. The resulting algorithm has been added to the TIGRE toolbox, a repository for iterative reconstruction algorithms for general CT, but this memory-saving and problem-splitting strategy can be easily adapted for use with other GPU-based tomographic reconstruction code.

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More information

Accepted/In Press date: 12 July 2020
e-pub ahead of print date: 29 July 2020
Published date: December 2020
Keywords: Computed Tomography, Iterative reconstruction, Software, multi-GPU

Identifiers

Local EPrints ID: 434722
URI: http://eprints.soton.ac.uk/id/eprint/434722
ISSN: 0743-7315
PURE UUID: 9a768f33-b41e-4e67-a537-7af9a9b7125e
ORCID for Richard Boardman: ORCID iD orcid.org/0000-0002-4008-0098

Catalogue record

Date deposited: 07 Oct 2019 16:30
Last modified: 26 Nov 2021 06:13

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Contributors

Author: Ander Biguri
Author: Reuben J. Lindroos
Author: Robert Bryll
Author: Hossein Towsyfyan
Author: Hans Deyhle
Author: Ibrahim Harrane
Author: Mark Mavrogordato
Author: Manjit Dosanjh
Author: Steven Hancock

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