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Model-based Iterative CT Image Reconstruction on GPUs

Published: 26 January 2017 Publication History

Abstract

Computed Tomography (CT) Image Reconstruction is an important technique used in a variety of domains, including medical imaging, electron microscopy, non-destructive testing and transportation security. Model-based Iterative Reconstruction (MBIR) using Iterative Coordinate Descent (ICD) is a CT algorithm that produces state-of-the-art results in terms of image quality. However, MBIR is highly computationally intensive and challenging to parallelize, and has traditionally been viewed as impractical in applications where reconstruction time is critical. We present the first GPU-based algorithm for ICD-based MBIR. The algorithm leverages the recently-proposed concept of SuperVoxels, and efficiently exploits the three levels of parallelism available in MBIR to better utilize the GPU hardware resources. We also explore data layout transformations to obtain more coalesced accesses and several GPU-specific optimizations for MBIR that boost performance. Across a suite of 3200 test cases, our GPU implementation obtains a geometric mean speedup of 4.43X over a state-of-the-art multi-core implementation on a 16-core iso-power CPU.

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Information

Published In

cover image ACM SIGPLAN Notices
ACM SIGPLAN Notices  Volume 52, Issue 8
PPoPP '17
August 2017
442 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3155284
Issue’s Table of Contents
  • cover image ACM Conferences
    PPoPP '17: Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
    January 2017
    476 pages
    ISBN:9781450344937
    DOI:10.1145/3018743
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 January 2017
Published in SIGPLAN Volume 52, Issue 8

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Author Tags

  1. computed tomography
  2. graphics processing units
  3. iterative coordinate descent
  4. model based iterative reconstruction

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  • (2024)Comparison of iterative reconstruction implementations for multislice helical CTZeitschrift für Medizinische Physik10.1016/j.zemedi.2024.04.001Online publication date: Apr-2024
  • (2023)Convolutional Forward Models for X-Ray Computed TomographySIAM Journal on Imaging Sciences10.1137/21M146419116:4(1953-1977)Online publication date: 12-Oct-2023
  • (2021)Scalable FBP decomposition for cone-beam CT reconstructionProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476139(1-16)Online publication date: 14-Nov-2021
  • (2021)Performance portable back-projection algorithms on CPUsProceedings of the 35th ACM International Conference on Supercomputing10.1145/3447818.3460353(316-328)Online publication date: 3-Jun-2021
  • (2021)An Efficient One-Step Method for Spectral CT Based on an Approximate Linear ModelIEEE Transactions on Radiation and Plasma Medical Sciences10.1109/TRPMS.2020.30155985:4(528-536)Online publication date: Jul-2021
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