@inproceedings{8db300bb84344ec5b876713a628ce535,
title = "MAPEM-Net: An unrolled neural network for Fully 3D PET image reconstruction",
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.",
keywords = "PET, Penalized image reconstruction, Unrolled neural network",
author = "Kuang Gong and Dufan Wu and Kyungsang Kim and Jaewon Yang and Tao Sun and {El Fakhri}, Georges and Youngho Seo and Quanzheng Li",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 ; Conference date: 02-06-2019 Through 06-06-2019",
year = "2019",
doi = "10.1117/12.2534904",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Samuel Matej and Metzler, {Scott D.}",
booktitle = "15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine",
}