FlowMRI-Net: A Generalizable Self-Supervised 4D Flow MRI Reconstruction Network.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Published Date: May 16, 2025

Abstract

BACKGROUND: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications.

Authors

  • Luuk Jacobs
    Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands.
  • Marco Piccirelli
    Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland.
  • Valery Vishnevskiy
    Institute for Biomedical Engineering, University and ETH Zurich, ETZ F 95, Gloriastrasse 35, 8092 Zürich, Switzerland. Electronic address: vishnevskiy@biomed.ee.ethz.ch.
  • Sebastian Kozerke

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