Massively parallel 3D image reconstruction

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 res...

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Bibliographic Details
Published in:International Conference for High Performance Computing, Networking, Storage and Analysis (Online) pp. 1 - 12
Main Authors: Wang, Xiao, Sabne, Amit, Sakdhnagool, Putt, Kisner, Sherman J., Bouman, Charles A., Midkiff, Samuel P.
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 12.11.2017
Series:ACM Conferences
Subjects:
Computed tomography
Computing methodologies > Computer graphics > Image manipulation > Image processing
Computing methodologies > Parallel computing methodologies > Parallel algorithms > Massively parallel algorithms
Iterative methods
Parallel algorithms
Reconstruction algorithms
Security
Technological innovation
Three-dimensional displays
ISBN:9781450351140, 145035114X
ISSN:2167-4337
Online Access:Get full text
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Summary: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.
ISBN:9781450351140
145035114X
ISSN:2167-4337
DOI:10.1145/3126908.3126911