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Computed Tomography (CT) Image Reconstruction is an important technique used in a wide range of applications, ranging from explosive detection, medical imaging to scientific imaging. Among available reconstruction methods, Model Based Iterative Reconstruction (MBIR) produces higher quality images and allows for the use of more general CT scanner geometries than is possible with more commonly used methods. The high computational cost of MBIR, however, often makes it impractical in applications for which it would otherwise be ideal. This paper describes a new MBIR implementation that significantly reduces the computational cost of MBIR while retaining its benefits. It describes a novel organization of the scanner data into super-voxels (SV) that, combined with a super-voxel buffer (SVB), dramatically increase locality and prefetching, enable parallelism across SVs and lead to an average speedup of 187 on 20 cores.

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Computed Tomographic (CT) image reconstruction is an important technique used in a wide range of applications. Among reconstruction methods, Model-Based Iterative Reconstruction (MBIR) is known to produce much higher quality CT images; however, the high computational requirements of MBIR greatly restrict their application. Currently, MBIR speed is primarily limited by irregular data access patterns, the difficulty of effective parallelization, and slow algorithmic convergence. This paper presents a new algorithm for MBIR, the Non-Uniform Parallel Super-Voxel (NU-PSV) algorithm, that regularizes the data access pattern, enables massive parallelism, and ensures fast convergence. We compare the NU-PSV algorithm with two state-of-the-art implementations on a 69632-core distributed system. Results indicate that the NU-PSV algorithm has an average speedup of 1665 compared to the fastest state-of-the-art implementations.

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