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Title: Scalable FBP decomposition for cone-beam CT reconstruction

Abstract

Filtered Back-Projection (FBP) is a fundamental compute intense algorithm used in tomographic image reconstruction. Cone-Beam Computed Tomography (CBCT) devices use a cone-shaped X-ray beam, in comparison to the parallel beam used in older CT generations. Distributed image reconstruction of cone-beam datasets typically relies on dividing batches of images into different nodes. This simple input decomposition, however, introduces limits on input/output sizes and scalability.We propose a novel decomposition scheme and reconstruction algorithm for distributed FPB. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline and improves the scalability by replacing two communication collectives with only one segmented reduction. Finally, we implement the proposed decomposition scheme in a framework that is useful for all current-generation CT devices (7th gen). In our experiments using up to 1024 GPUs, our framework can construct 40963 volumes, for real-world datasets, in under 16 seconds (including I/O).

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [1];  [1];  [1]
  1. AIST, Japan
  2. University College London (UCL), UK
  3. ORNL
  4. University of Southhampton
  5. University of Southampton, UK
  6. RIKEN Laboratory
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1862116
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21) - St. Louis, Missouri, United States of America - 11/14/2021 6:00:00 PM-11/19/2021 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Chen, Peng, Biguri, Ander, Wang, Xiao, Boardman, Richard, Blumensath, Thomas, Matsuoka, Satoshi, Wahib, Mohamed, Hirofuchi, Takahiro, and Ogawa, Hirotaka. Scalable FBP decomposition for cone-beam CT reconstruction. United States: N. p., 2021. Web. doi:10.1145/3458817.3476139.
Chen, Peng, Biguri, Ander, Wang, Xiao, Boardman, Richard, Blumensath, Thomas, Matsuoka, Satoshi, Wahib, Mohamed, Hirofuchi, Takahiro, & Ogawa, Hirotaka. Scalable FBP decomposition for cone-beam CT reconstruction. United States. https://doi.org/10.1145/3458817.3476139
Chen, Peng, Biguri, Ander, Wang, Xiao, Boardman, Richard, Blumensath, Thomas, Matsuoka, Satoshi, Wahib, Mohamed, Hirofuchi, Takahiro, and Ogawa, Hirotaka. 2021. "Scalable FBP decomposition for cone-beam CT reconstruction". United States. https://doi.org/10.1145/3458817.3476139. https://www.osti.gov/servlets/purl/1862116.
@article{osti_1862116,
title = {Scalable FBP decomposition for cone-beam CT reconstruction},
author = {Chen, Peng and Biguri, Ander and Wang, Xiao and Boardman, Richard and Blumensath, Thomas and Matsuoka, Satoshi and Wahib, Mohamed and Hirofuchi, Takahiro and Ogawa, Hirotaka},
abstractNote = {Filtered Back-Projection (FBP) is a fundamental compute intense algorithm used in tomographic image reconstruction. Cone-Beam Computed Tomography (CBCT) devices use a cone-shaped X-ray beam, in comparison to the parallel beam used in older CT generations. Distributed image reconstruction of cone-beam datasets typically relies on dividing batches of images into different nodes. This simple input decomposition, however, introduces limits on input/output sizes and scalability.We propose a novel decomposition scheme and reconstruction algorithm for distributed FPB. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline and improves the scalability by replacing two communication collectives with only one segmented reduction. Finally, we implement the proposed decomposition scheme in a framework that is useful for all current-generation CT devices (7th gen). In our experiments using up to 1024 GPUs, our framework can construct 40963 volumes, for real-world datasets, in under 16 seconds (including I/O).},
doi = {10.1145/3458817.3476139},
url = {https://www.osti.gov/biblio/1862116}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {11}
}

Conference:
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