Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 6, 2025

Abstract

PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation.

Authors

  • Hanzhong Wang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Qiaoyi Xue
    Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Xiaoya Qiao
  • Zengping Lin
    Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
  • Jiaxu Zheng
    Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China.
  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Min Zhang
    Department of Infectious Disease, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Qiu Huang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Yanqi Huang
    Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
  • Tuoyu Cao
    Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.
  • Jin Wang
    Cells Vision (Guangzhou) Medical Technology Inc., Guangzhou, China. Electronic address: wangjin@cellsvision.com.
  • 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.

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