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High performance model based image reconstruction

Published:27 February 2016Publication History
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Abstract

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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            • Published in

              cover image ACM SIGPLAN Notices
              ACM SIGPLAN Notices  Volume 51, Issue 8
              PPoPP '16
              August 2016
              405 pages
              ISSN:0362-1340
              EISSN:1558-1160
              DOI:10.1145/3016078
              Issue’s Table of Contents
              • cover image ACM Conferences
                PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
                February 2016
                420 pages
                ISBN:9781450340922
                DOI:10.1145/2851141

              Copyright © 2016 ACM

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              • Published: 27 February 2016

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