Direct parametric reconstruction in dynamic PET using deep image prior and a novel parameter magnification strategy.

Journal: Computers in biology and medicine
Published Date: Jun 2, 2025

Abstract

BACKGROUND/PURPOSE: Multiple parametric imaging in positron emission tomography (PET) is challenging due to the noisy dynamic data and the complex mapping to kinetic parameters. Although methods like direct parametric reconstruction have been proposed to improve the image quality, limitations persist, particularly for nonlinear and small-value micro-parameters (e.g., k, k). This study presents a novel unsupervised deep learning approach to reconstruct and improve the quality of these micro-parameters.

Authors

  • Xiaotong Hong
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Fanghu Wang
    The WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, China.
  • Hao Sun
    Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
  • Lijun Lu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

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