Deep learning-based PET image denoising and reconstruction: a review.

Journal: Radiological physics and technology
Published Date: Feb 6, 2024

Abstract

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

Authors

  • Fumio Hashimoto
    Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, 434-8601, Japan. fumio.hashimoto@crl.hpk.co.jp.
  • Yuya Onishi
    Graduate School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan.
  • Kibo Ote
    Central Research Laboratory, Hamamatsu Photonics K. K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu 434-8601, Japan.
  • Hideaki Tashima
    National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-Ku, Chiba, 263-8555, Japan.
  • Andrew J Reader
    School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom; and.
  • Taiga Yamaya
    National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan.

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