Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.

Journal: Medical physics
Published Date: Jul 6, 2022

Abstract

PURPOSE: To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.

Authors

  • Lianli Liu
    Department of Radiation Oncology, Stanford University, Palo Alto, California, USA.
  • Liyue Shen
    Department of Radiation Oncology, Stanford University, Stanford, California.
  • Adam Johansson
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • James M Balter
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • Yue Cao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
  • Daniel Chang
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals