Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy.

Journal: Medical physics
Published Date: Nov 1, 2021

Abstract

PURPOSE: High-dose-rate (HDR) prostate brachytherapy involves treatment catheter placement, which is currently empirical and physician dependent. The lack of proper catheter placement guidance during the procedure has left the physicians to rely on a heuristic thinking-while-doing technique, which may cause large catheter placement variation and increased plan quality uncertainty. Therefore, the achievable dose distribution could not be quantified prior to the catheter placement. To overcome this challenge, we proposed a learning-based method to provide HDR catheter placement guidance for prostate cancer patients undergoing HDR brachytherapy.

Authors

  • Yang Lei
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Yabo Fu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Justin Roper
    Radiology Oncology, Emory University, 1365 Clifton Road, Department of Radiation Oncology, Atlanta, Atlanta, Georgia, 30322, UNITED STATES.
  • Ashesh B Jani
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Pretesh Patel
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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