The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.

Journal: Journal of the American College of Radiology : JACR
Published Date: Sep 1, 2019

Abstract

Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths that can potentially benefit radiology. Specifically, adversarial networks have the potential to decrease radiation exposure to patients through minimizing repeat imaging due to artifact, decreasing acquisition time, and generating higher quality images from low-dose or no-dose studies. The authors provide an overview of a specific type of adversarial network called a "generalized adversarial network" and review its uses in current medical imaging research.

Authors

  • Brianna L Vey
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: bvey@emory.edu.
  • Judy W Gichoya
    The Johns Hopkins Hospital, Department of Radiology, 601 N Caroline St, Room 4223, Baltimore, MD 21287 (S.K.); Cleveland Clinic, Department of Radiation Oncology, Cleveland, Ohio (H.E.); Emory University School of Medicine, Department of Radiology, Atlanta, Georgia (J.G.); University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania (C.E.K.).
  • Adam Prater
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • C Matthew Hawkins
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.

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