Deep learning-based methods may minimize GBCA dosage in brain MRI.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To evaluate the clinical performance of a deep learning (DL)-based method for brain MRI exams with reduced gadolinium-based contrast agent (GBCA) dose to provide better understanding of the readiness and limitations of this method.

Authors

  • Huanyu Luo
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, the West Southern 4th Ring Road, Fengtai District, Beijing, 100070, China.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.
  • Nan-Jie Gong
    Vector Lab for Intelligent Medical Imaging and Neural Engineering, International Innovation Center of Tsinghua University, Shanghai, China.
  • Jonthan Tamir
    Subtle Medical Inc., Menlo Park, CA, USA.
  • Srivathsa Pasumarthi Venkata
    Subtle Medical Inc., Menlo Park, CA, USA.
  • Cheng Xu
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, Australia.
  • Yunyun Duan
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Tao Zhou
    Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Fuqing Zhou
    Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang 330006, China; Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, China. Electronic address: fq.chou@yahoo.com.
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.
  • Jing Xue
    Shandong University School of Medicine, Shandong University Jinan, P. R. China.
  • Yaou Liu
    Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China.