Effective deep-learning brain MRI super resolution using simulated training data.

Journal: Computers in biology and medicine
Published Date: Oct 31, 2024

Abstract

BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR imaging is prone to artifacts and is not universally available. Consequently, low-resolution (LR) MRI images are typically acquired. Deep learning (DL)-based super-resolution (SR) techniques can transform LR images into HR quality. However, these techniques require paired HR-LR data for training the SR networks.

Authors

  • Aymen Ayaz
    Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, the Netherlands. Electronic address: a.ayaz@tue.nl.
  • Rien Boonstoppel
    Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands. Electronic address: d.j.boonstoppel@student.tue.nl.
  • Cristian Lorenz
    Philips Research Laboratories, Hamburg, Germany. Electronic address: cristian.lorenz@philips.com.
  • Juergen Weese
    Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany.
  • Josien Pluim
  • Marcel Breeuwer
    Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, The Netherlands.

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