Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network.

Journal: Medical physics
Published Date: May 1, 2019

Abstract

PURPOSE: Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration.

Authors

  • Yuncheng Zhong
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Yevgeniy Vinogradskiy
    Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
  • Liyuan Chen
  • Nick Myziuk
    Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA.
  • Richard Castillo
    Emory University, Department of Radiation Oncology, Atlanta, United States.
  • Edward Castillo
    Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States.
  • Thomas Guerrero
    Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States.
  • Steve Jiang
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.

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