Cuda Parallel Implementation of Image Reconstruction Algorithm for Positron Emission Tomography
Belzunce MA1, 2, *, Verrastro CA1, 2, Venialgo E1, 2, Cohen IM1, 3
Identifiers and Pagination:Year: 2012
First Page: 108
Last Page: 118
Publisher Id: TOMIJ-6-108
Article History:Received Date: 12/04/2012
Revision Received Date: 09/08/2012
Acceptance Date: 25/08/2012
Electronic publication date: 14/12/2012
Collection year: 2012
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Although the use of iterative algorithms for image reconstruction in 3D Positron Emission Tomography (PET) has shown to produce images with better quality than analytical methods, they are computationally expensive. New Graphic Processor Units (GPUs) provide high performance at low cost and programming tools that make it possible to execute parallel algorithms in scientific applications. In this work, a GPU parallel implementation of the iterative reconstruction algorithm MLEM 3D has been developed using CUDA, a parallel model from NVIDIA. The Siddon algorithm was used as Projector and Backprojector. Acceleration factors up to 85 times were achieved, with respect to a single thread CPU implementation. The performance in GPU with Tesla and Fermi, which are respectively the first and the last generation of CUDA compatible architectures, has been compared. The image quality in each platform has been analyzed, showing a higher level of noise in GPU, due to race condition problems. The new features of Fermi architecture permitted to solve this problem using atomic operations.