Deep Learning Subtraction Angiography: Improved Generalizability with Transfer Learning.

Journal: Journal of vascular and interventional radiology : JVIR
Published Date: Dec 16, 2022

Abstract

PURPOSE: To investigate the utility and generalizability of deep learning subtraction angiography (DLSA) for generating synthetic digital subtraction angiography (DSA) images without misalignment artifacts.

Authors

  • Brendan T Crabb
    Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA.
  • Forrest Hamrick
    Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA.
  • Tyler Richards
    Department of Radiology and Imaging Sciences, University of Utah School of Medicine, Salt Lake City, Utah.
  • Preston Eiswirth
    Department of Radiology and Imaging Sciences, University of Utah School of Medicine, Salt Lake City, Utah.
  • Frederic Noo
    Department of Radiology and Imaging Sciences, The University of Utah, Salt Lake City, UT, 84108, USA.
  • Albert Hsiao
    Department of Radiology, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., S.J.K., K.E.J., A.C.Y., S.S.B., L.D.H., A.H.); and Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.).
  • Gabriel C Fine
    Department of Radiology and Imaging Sciences, University of Utah School of Medicine, Salt Lake City, Utah.

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