A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

Journal: Medical physics
Published Date: Apr 3, 2020

Abstract

PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation.

Authors

  • Zhao Peng
    Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei, Anhui, China.
  • Xi Fang
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Pingkun Yan
    Philips Research North America, Briarcliff Manor, NY 10510, USA.
  • Hongming Shan
  • Tianyu Liu
    Department of Automation, Tsinghua University,Beijing, China.
  • Xi Pei
    Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei, Anhui, China.
  • Ge Wang
    Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Bob Liu
    Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.
  • Mannudeep K Kalra
  • X George Xu
    Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei, Anhui, China.

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