Abstract #0601

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

Zihao Chen^1,2, Yuhua Chen^1,2, Ankur Saini³, William Devine³, Yibin Xie¹, Cecilia Lo³, Debiao Li^1,2, 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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