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Tomography
Volume 6
Issue 1
10.18383/j.tom.2020.00011
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Article

Efficient CT Image Reconstruction in a GPU Parallel Environment

by
Tomás A. Valencia Pérez
1,
Javier M. Hernández López
1,
Eduardo Moreno-Barbosa
1,
Benito de Celis Alonso
1,
Martín R. Palomino Merino
1 and
Victor M. Castaño Meneses
2,*
1
Faculty of Mathematical and Physical Sciences, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
2
Molecular and Materials, Engineering Department, Universidad Nacional Autónoma de México, Queretaro 76230, Mexico
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(1), 44-53; https://doi.org/10.18383/j.tom.2020.00011
Submission received: 14 December 2019 / Revised: 9 January 2020 / Accepted: 5 February 2020 / Published: 1 March 2020
Versions Notes

Abstract

Computed tomography is nowadays an indispensable tool in medicine used to diagnose multiple diseases. In clinical and emergency room environments, the speed of acquisition and information processing are crucial. CUDA is a software architecture used to work with NVIDIA graphics processing units. In this paper a methodology to accelerate tomographic image reconstruction based on maximum likelihood expectation maximization iterative algorithm and combined with the use of graphics processing units programmed in CUDA framework is presented. Implementations developed here are used to reconstruct images with clinical use. Timewise, parallel versions showed improvement with respect to serial implementations. These differences reached, in some cases, 2 orders of magnitude in time while preserving image quality. The image quality and reconstruction times were not affected significantly by the addition of Poisson noise to projections. Furthermore, our implementations showed good performance when compared with reconstruction methods provided by commercial software. One of the goals of this work was to provide a fast, portable, simple, and cheap image reconstruction system, and our results support the statement that the goal was achieved.
Keywords: Computed tomography; iterative algorithms; GPU; parallelization; reconstruction; image quality Computed tomography; iterative algorithms; GPU; parallelization; reconstruction; image quality

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MDPI and ACS Style

Pérez, T.A.V.; López, J.M.H.; Moreno-Barbosa, E.; Alonso, B.d.C.; Merino, M.R.P.; Meneses, V.M.C. Efficient CT Image Reconstruction in a GPU Parallel Environment. Tomography 2020, 6, 44-53. https://doi.org/10.18383/j.tom.2020.00011

AMA Style

Pérez TAV, López JMH, Moreno-Barbosa E, Alonso BdC, Merino MRP, Meneses VMC. Efficient CT Image Reconstruction in a GPU Parallel Environment. Tomography. 2020; 6(1):44-53. https://doi.org/10.18383/j.tom.2020.00011

Chicago/Turabian Style

Pérez, Tomás A. Valencia, Javier M. Hernández López, Eduardo Moreno-Barbosa, Benito de Celis Alonso, Martín R. Palomino Merino, and Victor M. Castaño Meneses. 2020. "Efficient CT Image Reconstruction in a GPU Parallel Environment" Tomography 6, no. 1: 44-53. https://doi.org/10.18383/j.tom.2020.00011

APA Style

Pérez, T. A. V., López, J. M. H., Moreno-Barbosa, E., Alonso, B. d. C., Merino, M. R. P., & Meneses, V. M. C. (2020). Efficient CT Image Reconstruction in a GPU Parallel Environment. Tomography, 6(1), 44-53. https://doi.org/10.18383/j.tom.2020.00011

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