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Abstract #0302

T2-deblurred deep learning super-resolution for turbo spin echo MRI

Zihao Chen1,2, Yibin Xie1, Debiao Li1,2, Yijen Wu3, and Anthony Christodoulou1,2
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 3Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United States


Deep learning MR super-resolution (SR) is a promising approach to reduce scan time without requiring custom sequences and iterative reconstruction. However, previous SR approaches are incompatible with turbo spin echo (TSE) sequence due to differences in T2 blurring effects between high-resolution and low-resolution TSE images. Here we propose a T2-deblurred deep learning SR model for 3x3 in-plane super-resolution of 3D TSE images. Our method accelerated scanning by 9x in both retrospective and prospective testing and provided better image quality than previous SR methods.

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