Generation of virtual lung single-photon emission computed tomography/CT fusion images for functional avoidance radiotherapy planning using machine learning algorithms.

Journal: Journal of medical imaging and radiation oncology
Published Date: Mar 15, 2019

Abstract

INTRODUCTION: Functional image-guided radiotherapy (RT) planning for normal lung avoidance has recently been introduced. Single-photon emission computed tomography (SPECT)/CT can help identify the functional areas of lungs, but it is associated with delayed treatment time, additional costs and unexpected radiation exposure. In this study, we propose a machine learning algorithm that can generate functional chest CT images using the conditional generative adversarial networks (cGANs).

Authors

  • Bum-Sup Jang
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.
  • Ji Hyun Chang
    Department of Radiation Oncology, SMG-SNU Boramae Medical Center, Seoul, Korea.
  • Andrew J Park
    Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire; Tuck School of Business, Dartmouth College, Hanover, New Hampshire.
  • Hong-Gyun Wu
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.

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