Iterative PET image reconstruction using convolutional neural network representation

Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Jaewon Yang, Youngho Seo, Georges El Fakhri, Jinyi Qi, Quanzheng Li

Research output: Contribution to journalArticlepeer-review

142 Scopus citations


PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently, the deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this paper, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool. We formulate the objective function as a constrained optimization problem and solve it using the alternating direction method of multipliers algorithm. Both simulation data and hybrid real data are used to evaluate the proposed method. Quantification results show that our proposed iterative neural network method can outperform the neural network denoising and conventional penalized maximum likelihood methods.

Original languageEnglish (US)
Article number8463596
Pages (from-to)675-685
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number3
StatePublished - Mar 2019
Externally publishedYes


  • Positron emission tomography
  • convolutional neural network
  • iterative reconstruction

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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