Whole-body Composition Profiling Using a Deep Learning Algorithm: Influence of Different Acquisition Parameters on Algorithm Performance and Robustness.

Journal: Investigative radiology
Published Date: Jan 1, 2022

Abstract

OBJECTIVES: To develop, test, and validate a body composition profiling algorithm for automated segmentation of body compartments in whole-body magnetic resonance imaging (wbMRI) and to investigate the influence of different acquisition parameters on performance and robustness.

Authors

  • Florian A Huber
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland. Electronic address: florian.huber@usz.ch.
  • Krishna Chaitanya
    Biomedical Image Computing Group, ETH Zurich, Zurich 8092, Switzerland.
  • Nico Gross
    From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, and Faculty of Medicine, University of Zurich.
  • Sunand Reddy Chinnareddy
    From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, and Faculty of Medicine, University of Zurich.
  • Felix Gross
    From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, and Faculty of Medicine, University of Zurich.
  • Ender Konukoglu
  • Roman Guggenberger
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Ramistrasse 100, 8091, Zurich, Switzerland.

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