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

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

12 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans
ISBN (Electronic)9781510625433
StatePublished - 2019
Externally publishedYes
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Physics of Medical Imaging
Country/TerritoryUnited States
CitySan Diego


  • Image reconstruction
  • PET
  • Unrolled neural network

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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