Virtual high-count PET image generation using a deep learning method.

Journal: Medical physics
Published Date: Aug 13, 2022

Abstract

PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images.

Authors

  • Juan Liu
    Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, PR China. Electronic address: liujuan@whu.edu.cn.
  • Sijin Ren
    Department of Radiology and Biomedical Imaging, Yale University, United States of America.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Niloufarsadat Mirian
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Yu-Jung Tsai
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
  • Michal Kulon
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Darko Pucar
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
  • Ming-Kai Chen
    Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
  • Chi Liu

Keywords

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