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Abstract #0601

Deep learning super-resolution for sub-50-micron MRI of genetically engineered mouse embryos

Zihao Chen1,2, Yuhua Chen1,2, Ankur Saini3, William Devine3, Yibin Xie1, Cecilia Lo3, Debiao Li1,2, Yijen Wu3, and Anthony Christodoulou1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, UCLA, Los Angeles, CA, United States, 3Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United States

Genetically engineered mouse models (GEMM) are indispensable in modeling human diseases. High resolution MRI with spatial resolution less than 100 μm has made incredible progress for phenotyping mouse embryos. However, it takes more than 10 hours' acquisition time to reach such high resolution, so reducing the scan time is of great need. Here we propose a deep learning based super-resolution approach for 3x3 super-resolution (SR) of mouse embryo images using raw k-space data. Our method can reduce the scan time by a factor of 9 while preserving the diagnostic details and shows better quantitative results than previous SR methods.

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