Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.

Journal: Medical physics
Published Date: Sep 13, 2023

Abstract

BACKGROUND: Real-time liver imaging is challenged by the short imaging time (within hundreds of milliseconds) to meet the temporal constraint posted by rapid patient breathing, resulting in extreme under-sampling for desired 3D imaging. Deep learning (DL)-based real-time imaging/motion estimation techniques are emerging as promising solutions, which can use a single X-ray projection to estimate 3D moving liver volumes by solved deformable motion. However, such techniques were mostly developed for a specific, fixed X-ray projection angle, thereby impractical to verify and guide arc-based radiotherapy with continuous gantry rotation.

Authors

  • Hua-Chieh Shao
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Yunxiang Li
    The Key Lab of RF Circuits and Systems of Ministry of Education, Microelectronics CAD Center, Hangzhou Dianzi University, Hangzhou, 310018, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Steve Jiang
  • You Zhang
    Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Keywords

External Resources

Popular Topics
Recent Journals