A review on medical imaging synthesis using deep learning and its clinical applications.

Journal: Journal of applied clinical medical physics
Published Date: Dec 11, 2020

Abstract

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Authors

  • Tonghe Wang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Yang Lei
    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.
  • Jacob F Wynne
    Department of Radiation Oncology, Emory University, Atlanta, GA, USA.
  • Walter J Curran
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
  • Tian Liu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.

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