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MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction
Author(s): Kuang Gong; Dufan Wu; Kyungsang Kim; Jaewon Yang; Tao Sun; 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 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.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110720O (28 May 2019); doi: 10.1117/12.2534904
Show Author Affiliations
Kuang Gong, Massachusetts General Hospital and Harvard Medical School (United States)
Dufan Wu, Massachusetts General Hospital and Harvard Medical School (United States)
Kyungsang Kim, Massachusetts General Hospital and Harvard Medical School (United States)
Jaewon Yang, Univ. of California, San Francisco (United States)
Tao Sun, Massachusetts General Hospital and Harvard Medical School (United States)
Georges El Fakhri, Massachusetts General Hospital and Harvard Medical School (United States)
Youngho Seo, Univ. of California, San Francisco (United States)
Quanzheng Li, Massachusetts General Hospital and Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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