Moving Beyond CT Body Composition Analysis: Using Style Transfer for Bringing CT-Based Fully-Automated Body Composition Analysis to T2-Weighted MRI Sequences.

Journal: Investigative radiology
Published Date: Feb 18, 2025

Abstract

OBJECTIVES: Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.

Authors

  • Johannes Haubold
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. Johannes.haubold@uk-essen.de.
  • Olivia Barbara Pollok
  • Mathias Holtkamp
  • Luca Salhöfer
  • Cynthia Sabrina Schmidt
    Institute for Artificial Intelligence in Medicine, University Medicine Essen, Essen, Germany.
  • Christian Bojahr
  • Jannis Straus
  • Benedikt Michael Schaarschmidt
    University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany. Electronic address: Benedikt.Schaarschmidt@med.uni-duesseldorf.de.
  • Katarzyna Borys
    Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany. Electronic address: Katarzyna.Borys@uk-essen.de.
  • Judith Kohnke
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
  • Yutong Wen
    Data Integration Center, Central IT Department, University Hospital Essen, Essen, Germany.
  • Marcel Opitz
  • Lale Umutlu
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, Departments of.
  • Michael Forsting
    Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
  • Christoph M Friedrich
    Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • René Hosch
    Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany.

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