Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

• Published in: BMC research notes Vol. 7; no. 1; p. 582
• Main Authors: Leeser, Miriam, Mukherjee, Saoni, Brock, James
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.08.2014; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Analysis; Biomedical and Life Sciences; Biomedicine; Codes; CT imaging; Life Sciences; Medical Imaging; Medicine/Public Health; Open source software; Programming languages; Protein folding; Sensors; Studies; Technical Note; Tomography; Tomography, X-Ray Computed - methods; CUDA; OpenCL; Computed tomography; Conebeam reconstruction; Graphics processing unit
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/1756-0500-7-582

Background Biomedical image reconstruction applications require producing high fidelity images in or close to real-time. We have implemented reconstruction of three dimensional conebeam computed tomography(CBCT) with two dimensional projections. The algorithm takes slices of the target, weights and filters them to backproject the data, then creates the final 3D volume. We have implemented the algorithm using several hardware and software approaches and taken advantage of different types of parallelism in modern processors. The two hardware platforms used are a Central Processing Unit (CPU) and a heterogeneous system with a combination of CPU and GPU. On the CPU we implement serial MATLAB, parallel MATLAB, C and parallel C with OpenMP extensions. These codes are compared against the heterogeneous versions written in CUDA-C and OpenCL. Findings Our results show that GPUs are particularly well suited to accelerating CBCT. Relative performance was evaluated on a mathematical phantom as well as on mouse data. Speedups of up to 200x are observed by using an AMD GPU compared to a parallel version in C with OpenMP constructs. Conclusions In this paper, we have implemented the Feldkamp-Davis-Kress algorithm, compatible with Fessler’s image reconstruction toolbox and tested it on different hardware platforms including CPU and a combination of CPU and GPU. Both NVIDIA and AMD GPUs have been used for performance evaluation. GPUs provide significant speedup over the parallel CPU version.