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Accelerating PET Image Reconstruction with CUDA

Published: 08 July 2022 Publication History

Abstract

Yale MOLAR is an in-house Positron Emission Tomography (PET) image reconstruction application written in C++ and MPI. It deals with hundreds of millions of lines-of-response (LORs) independently to reconstruct an image. The nature of the image reconstruction process makes MOLAR an ideal candidate for GPU acceleration. In this study, we present our work on accelerating MOLAR with CUDA, and show the results that demonstrate the effectiveness and correctness of our CUDA implementation. Overall, Yale MOLAR with CUDA runs up to 6 times faster than the CPU-only code, reducing a typical high resolution image reconstruction time from several hours to less than one hour.

References

[1]
W. Craig Barker, Shanthalaxmi Thada, and William Dieckmann. 2009. A GPU-accelerated implementation of the MOLAR PET reconstruction package. In 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). 4114–4119. https://doi.org/10.1109/NSSMIC.2009.5402353
[2]
C.A. Johnson, S. Thada, M. Rodriguez, Y. Zhao, A.R. Iano-Fletcher, J.-S. Liow, W.C. Barker, R.L. Martino, and R.E. Carson. 2004. Software architecture of the MOLAR-HRRT reconstruction engine. In IEEE Symposium Conference Record Nuclear Science 2004., Vol. 6. 3956–3960. https://doi.org/10.1109/NSSMIC.2004.1466744

Cited By

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  • (2025)Transition to GPU-based reconstruction for clinical organ-targeted PET scannerPhysics in Medicine & Biology10.1088/1361-6560/adb19870:5(055001)Online publication date: 14-Feb-2025

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Published In

cover image ACM Conferences
PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You
July 2022
455 pages
ISBN:9781450391610
DOI:10.1145/3491418
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2022

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Author Tags

  1. CUDA
  2. GPU
  3. Image reconstruction
  4. MOLAR
  5. PET

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  • Extended-abstract
  • Research
  • Refereed limited

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PEARC '22
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Overall Acceptance Rate 133 of 202 submissions, 66%

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Practice and Experience in Advanced Research Computing
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Cited By

View all
  • (2025)Transition to GPU-based reconstruction for clinical organ-targeted PET scannerPhysics in Medicine & Biology10.1088/1361-6560/adb19870:5(055001)Online publication date: 14-Feb-2025

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