Super-resolution musculoskeletal MRI using deep learning.

Journal: Magnetic resonance in medicine
Published Date: Nov 1, 2018

Abstract

PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

Authors

  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.
  • Zhongnan Fang
    LVIS Corporation, Palo Alto, California.
  • Feliks Kogan
    Department of Radiology, Stanford University, Stanford, California.
  • Jeff Wood
    Department of Radiology, Stanford University, Stanford, California.
  • Kathryn J Stevens
    Department of Radiology, Stanford University, Stanford, California.
  • Eric K Gibbons
    Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah.
  • Jin Hyung Lee
    Department of Bioengineering, Stanford University, Stanford, California.
  • Garry E Gold
    Department of Radiology, Stanford University, Stanford, California.
  • Brian A Hargreaves
    Department of Radiology, Stanford University, Stanford, California.

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