Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.

Journal: Cell reports. Medicine
Published Date: Jul 18, 2023

Abstract

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron emission tomography (PET), and low-dose computed tomography (CT) image generation tasks. First, in a retrospective MRI study (6,066 cases), we demonstrate its capability of handling 3- to 10-fold under-sampled MR data, enabling organ-level coverage with only 10- to 100-s scan time; second, a low-dose CT study (142 cases) shows that our framework can successfully alleviate the noise and streak artifacts in scans performed with only 10% radiation dose (0.61 mGy); and last, a fast whole-body PET study (131 cases) allows us to faithfully reconstruct tumor-induced lesions, including small ones (<4 mm), from 2- to 4-fold-accelerated PET acquisition (30-60 s/bp). This study offers a promising avenue for accurate and high-quality image reconstruction with broad clinical value.

Authors

  • Shu Liao
  • Zhanhao Mo
  • Mengsu Zeng
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Jiaojiao Wu
    Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine, Collaborative Innovation Center for Brain Science, Tongji University, 1800 Yuntai Road, Shanghai, 200123, China.
  • Yuning Gu
    School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China.
  • Guobin Li
    College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
  • Guotao Quan
  • Yang Lv
  • Lin Liu
    Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University; Shanghai Artificial Intelligence Laboratory.
  • Chun Yang
    State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan, 430074, China.
  • Xinglie Wang
    Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
  • Xiaoqian Huang
    Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Wenjing Cao
    School of Electrical Engineering and Automation, Tiangong University, Tianjin, China.
  • Yun Dong
    Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201800, China.
  • Ying Wei
    School of Information Science and Engineering, Northeastern University, Shenyang 110004, China ; Key Laboratory of Medical Imaging Calculation of the Ministry of Education, Shenyang 110004, China.
  • Qing Zhou
    Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Yongqin Xiao
    Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
  • Yiqiang Zhan
  • Xiang Sean Zhou
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.

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