A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Dec 24, 2021

Abstract

PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers.

Authors

  • Song Xue
  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Karl Peter Bohn
  • Jared Matzke
    Department of Informatics, Technical University of Munich, Munich, Germany.
  • Marco Viscione
  • Ian Alberts
    Department of Nuclear Medicine, University of Bern, Bern, Switzerland.
  • Hongping Meng
    Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chenwei Sun
    Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Miao Zhang
    gRED Computational Science, Genentech, Inc., South San Francisco, California.
  • Min Zhang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Raphael Sznitman
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Georges El Fakhri
  • Axel Rominger
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.

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