Effect of MR head coil geometry on deep-learning-based MR image reconstruction.

Journal: Magnetic resonance in medicine
PMID: 38647191

Abstract

PURPOSE: To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.

Authors

  • Natalia Dubljevic
    Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
  • Stephen Moore
    Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.
  • Michel Louis Lauzon
    Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.
  • Roberto Souza
    Medical Imaging and Computing Laboratory, Department of Computer Engineering and Industrial Automation, University of Campinas, Campinas, São Paulo, Brazil; Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Alberta, Canada; Seaman Family Magnetic Resonance Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Alberta, Canada. Electronic address: roberto.medeirosdeso@ucalgary.ca.
  • Richard Frayne
    Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Alberta, Canada; Seaman Family Magnetic Resonance Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, Alberta, Canada.

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