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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publication15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
EditorsSamuel Matej, Scott D. Metzler
PublisherSPIE
ISBN (Electronic)9781510628373
DOIs
StatePublished - 2019
Externally publishedYes
Event15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 - Philadelphia, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11072
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019
Country/TerritoryUnited States
CityPhiladelphia
Period6/2/196/6/19

Keywords

  • Penalized image reconstruction
  • PET
  • Unrolled neural network

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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