MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction

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

doi:10.1117/12.2534904

28 May 2019 MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction

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

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110720O (2019) https://doi.org/10.1117/12.2534904
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, 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 MAPEM algorithm, we propose a novel unrolled neural network framework for 3D PET image reconstruction. In this framework, the convolutional neural network is combined with the MAPEM update steps so that data consistency can be enforced. Both simulation and clinical datasets were used to evaluate the effectiveness of the proposed method. Quantification results show that our proposed MAPEM-Net method can outperform the neural network and Gaussian denoising methods.

Citation Download Citation

Kuang Gong, Dufan Wu, Kyungsang Kim, Jaewon Yang, Tao Sun, Georges El Fakhri, Youngho Seo, and Quanzheng Li "MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720O (28 May 2019); https://doi.org/10.1117/12.2534904

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