Low-count PET image reconstruction algorithm based on WGAN-GP
Authors: 
Ruiqi Fang, Ruipeng Guo, Min Zhao, Min YaoAuthors Info & Claims
Ruiqi Fang, Ruipeng Guo, Min Zhao, Min YaoAuthors Info & ClaimsPages 60 - 64
Abstract
Positron emission tomography (PET) technique can visualize the working status or fluid flow state inside opaque devices, and how to reconstruct high-quality images from low-count (LC) projection data with short scan time to meet the real-time online inspection remains an important research problem. A direct reconstruction algorithm CED-PET based on gradient-penalized Wasserstein Generative Adversarial Network (WGAN-GP) architecture is proposed. This network combines content loss, perceptual loss, and adversarial loss to achieve fast and high-quality reconstruction of low-count projection data. In addition, a special dataset for obtuse body bypassing was produced by combining Computational Fluid Dynamics (CFD) simulation software and the Geant4 Application for Tomographic Emission (GATE) simulation platform. The results on this dataset show that CED-PET can quickly reconstruct high-quality images with more realistic detail contours.
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February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 31 May 2023
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BIC 2023
BIC 2023: 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 10 - 12, 2023
Sanya, China
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