Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis.

Journal: Journal of X-ray science and technology
PMID: 40343882

Abstract

BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.

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.
  • Xing Chen
    School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China. xingchen@amss.ac.cn.
  • 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.
  • Zengping Lin
    Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, 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.
  • Guowei Feng
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Qiu Huang
    Department of Nuclear Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

Keywords

External Resources

Popular Topics
Recent Journals