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Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures

Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures
Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures

X-ray tomographic reconstruction typically uses voxel basis functions to represent volumetric images. Due to the structure in voxel basis representations, efficient ray-tracing methods exist allowing fast, GPU accelerated implementations. Tetrahedral mesh basis functions are a valuable alternative to voxel based image representations as they provide flexible, inhomogeneous partitions which can be used to provide reconstructions with reduced numbers of elements or with arbitrarily fine object surface representations. We thus present a robust parallelizable ray-tracing method for volumetric tetrahedral domains developed specifically for Computed Tomography image reconstruction. Tomographic image reconstruction requires algorithms that are robust to numerical errors in floating point arithmetic whilst typical data sizes encountered in tomography require the algorithm to be parallelisable in GPUs which leads to additional constraints on algorithm choices. Based on these considerations, this article presents numerical solutions to the design of efficient ray-tracing algorithms for the projection and backprojection operations. Initial reconstruction results using CAD data to define a triangulation of the domain demonstrate the advantages of our method and contrast tetrahedral mesh based reconstructions to voxel based methods.

Computed tomography, GPU, Ray tracing
0304-3991
1-9
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Towsyfyan, Hossein
f1f4fa2a-20e4-4519-a66b-2faecb50173d
Boardman, Richard
5818d677-5732-4e8a-a342-7164dbb10df1
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Towsyfyan, Hossein
f1f4fa2a-20e4-4519-a66b-2faecb50173d
Boardman, Richard
5818d677-5732-4e8a-a342-7164dbb10df1
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead

Biguri, Ander, Towsyfyan, Hossein, Boardman, Richard and Blumensath, Thomas (2020) Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures. Ultramicroscopy, 214, 1-9, [113016]. (doi:10.1016/j.ultramic.2020.113016).

Record type: Article

Abstract

X-ray tomographic reconstruction typically uses voxel basis functions to represent volumetric images. Due to the structure in voxel basis representations, efficient ray-tracing methods exist allowing fast, GPU accelerated implementations. Tetrahedral mesh basis functions are a valuable alternative to voxel based image representations as they provide flexible, inhomogeneous partitions which can be used to provide reconstructions with reduced numbers of elements or with arbitrarily fine object surface representations. We thus present a robust parallelizable ray-tracing method for volumetric tetrahedral domains developed specifically for Computed Tomography image reconstruction. Tomographic image reconstruction requires algorithms that are robust to numerical errors in floating point arithmetic whilst typical data sizes encountered in tomography require the algorithm to be parallelisable in GPUs which leads to additional constraints on algorithm choices. Based on these considerations, this article presents numerical solutions to the design of efficient ray-tracing algorithms for the projection and backprojection operations. Initial reconstruction results using CAD data to define a triangulation of the domain demonstrate the advantages of our method and contrast tetrahedral mesh based reconstructions to voxel based methods.

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Accepted/In Press date: 2 May 2020
Published date: July 2020
Additional Information: Funding Information: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This research was supported by EPSRC grant EP/R002495/1 and the European Metrology Research Programme through grant 17IND08. A. Biguri would like to thank Marco Vallario and Tobias Bertel for interesting and productive discussions on ray tracing and search trees. Publisher Copyright: © 2020 Elsevier B.V.
Keywords: Computed tomography, GPU, Ray tracing

Identifiers

Local EPrints ID: 434721
URI: http://eprints.soton.ac.uk/id/eprint/434721
ISSN: 0304-3991
PURE UUID: 97c10dad-ae2c-4a90-9747-0b641c52b249
ORCID for Richard Boardman: ORCID iD orcid.org/0000-0002-4008-0098
ORCID for Thomas Blumensath: ORCID iD orcid.org/0000-0002-7489-265X

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Date deposited: 07 Oct 2019 16:30
Last modified: 30 Jul 2022 04:01

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Contributors

Author: Ander Biguri
Author: Hossein Towsyfyan

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