Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Journal: The British journal of radiology
Published Date: Dec 1, 2023

Abstract

OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps.

Authors

  • Chih-Wei Chang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
  • Raanan Marants
    Department of Radiation Oncology, Brigham & Women's Hospital/Dana-Farber Cancer Institute/Harvard Medical School, Boston, Massachusetts, United States.
  • Yuan Gao
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Matthew Goette
    Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, Georgia, United States.
  • Jessica E Scholey
    Department of Radiation Oncology, University of California, San Francisco, CA.
  • Jeffrey D Bradley
    Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, Missouri.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Jun Zhou
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Atchar Sudhyadhom
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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