EMnet: an unrolled deep neural network for PET image reconstruction

Kuang Gong; Dufan Wu; Kyungsang Kim; Jaewon Yang; Georges El Fakhri; Youngho Seo; Quanzheng Li

doi:10.1117/12.2513096

1 March 2019 EMnet: an unrolled deep neural network for PET image reconstruction

Kuang Gong, Dufan Wu, Kyungsang Kim, Jaewon Yang, Georges El Fakhri, Youngho Seo, Quanzheng Li

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094853 (2019) https://doi.org/10.1117/12.2513096
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Kuang Gong, Dufan Wu, Kyungsang Kim, Jaewon Yang, Georges El Fakhri, Youngho Seo, and Quanzheng Li "EMnet: an unrolled deep neural network for PET image reconstruction", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094853 (1 March 2019); https://doi.org/10.1117/12.2513096

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