A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 39585354

Abstract

PURPOSE: Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging. This study aims to develop a wavelet-based DL method capable of restoring high-quality imaging from ultra-low-dose PET scans.

Authors

  • Song Xue
  • Fanxuan Liu
    Department of Nuclear Medicine, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
  • Hanzhong Wang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Hong Zhu
    Co-Innovation Center for the Sustainable Forestry in Southern China; Cerasus Research Center; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.
  • Hasan Sari
    Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America.
  • Marco Viscione
  • Raphael Sznitman
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
  • Axel Rominger
  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • 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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