Fully automated intracardiac 4D flow MRI post-processing using deep learning for biventricular segmentation.

Journal: European radiology
Published Date: Feb 17, 2022

Abstract

OBJECTIVES: 4D flow MRI allows for a comprehensive assessment of intracardiac blood flow, useful for assessing cardiovascular diseases, but post-processing requires time-consuming ventricular segmentation throughout the cardiac cycle and is prone to subjective errors. Here, we evaluate the use of automatic left and right ventricular (LV and RV) segmentation based on deep learning (DL) network that operates on short-axis cine bSSFP images.

Authors

  • Philip A Corrado
    Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA. pcorrado2@wisc.edu.
  • Andrew L Wentland
    University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
  • Jitka Starekova
    University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
  • Archana Dhyani
    University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
  • Kara N Goss
    UT Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX, 75390, USA.
  • Oliver Wieben
    Departments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, WI, USA.

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