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EMnet: an unrolled deep neural network for PET image reconstruction
Author(s): Kuang Gong; Dufan Wu; Kyungsang Kim; Jaewon Yang; Georges El Fakhri; Youngho Seo; Quanzheng Li
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Paper Abstract

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely applied to medical imaging denoising applications. In this work, based on the expectation maximization (EM) algorithm, we propose an unrolled neural network framework for PET image reconstruction, named EMnet. An innovative feature of the proposed framework is that the deep neural network is combined with the EM update steps in a whole graph. Thus data consistency can act as a constraint during network training. Both simulation data and real data are used to evaluate the proposed method. Quantification results show that our proposed EMnet method can outperform the neural network denoising and Gaussian denoising methods.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094853 (1 March 2019); doi: 10.1117/12.2513096
Show Author Affiliations
Kuang Gong, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Dufan Wu, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Kyungsang Kim, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Jaewon Yang, Univ. of California, San Francisco (United States)
Georges El Fakhri, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Youngho Seo, Univ. of California, San Francisco (United States)
Quanzheng Li, Massachusetts General Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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