A review on AI in PET imaging

Keisuke Matsubara, Masanobu Ibaraki, Mitsutaka Nemoto, Hiroshi Watabe, Yuichi Kimura

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.

Original languageEnglish
Pages (from-to)133-143
Number of pages11
JournalAnnals of Nuclear Medicine
Issue number2
Publication statusPublished - 2022 Feb


  • Artificial intelligence
  • Deep learning
  • PET

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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