BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jun 16, 2024

Abstract

PURPOSE: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based method for BVL-estimation.

Authors

  • Roland Opfer
    jung diagnostics GmbH, Hamburg, Germany.
  • Julia Krüger
    Jung diagnostics, Hamburg, Germany.
  • Thomas Buddenkotte
    Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany. Electronic address: t.buddenkotte@uke.de.
  • Lothar Spies
    jung diagnostics GmbH, Hamburg, Germany.
  • Finn Behrendt
    Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology, Hamburg, Germany.
  • Sven Schippling
    Multimodal Imaging in Neuroimmunological Diseases (MINDS), University of Zurich, Zurich, Switzerland.
  • Ralph Buchert
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. r.buchert@uke.de.

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