Physics-informed deep learning for T2-deblurred superresolution turbo spin echo MRI.

Journal: Magnetic resonance in medicine
Published Date: Aug 14, 2023

Abstract

PURPOSE: Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T relaxation effects in different k-space regions. To fill this gap, we developed a T -deblurred deep learning SR method for the SR of 3D-TSE images.

Authors

  • Zihao Chen
  • Margaret Caroline Stapleton
    Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Yibin Xie
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Debiao Li
  • Yijen L Wu
    Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Anthony G Christodoulou
    Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

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