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Abstract #3828

Accelerating Neuroradiology Protocols with Deep Learning MR Image Reconstruction. Which Methods Result in the Highest Perceived Image Quality?

Gregory Avey1, Laura Eisenmenger1, Nathan Kim1, Alexey Samsonov1, James Holmes1, Lloyd Estkowski2, and Tabassum Kennedy1
1University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, 2GE Healthcare, Madison, WI, United States

Deep Learning (DL) MRI image reconstruction holds great promise in improving overall MRI image quality and decreasing examination time. Given the nonlinear properties of DL reconstruction it is unknown which methods of decreasing MRI examination time are most suitable when adapting current protocols for DL based examination acceleration. 10 volunteers were scanned with clinical baseline T1 and T2 weighted FSE exams, along with 5 accelerated exam types. The resulting exams were scored to determine differences in image quality. DL image reconstruction allowed for an up to 78% reduction in scan time while matching baseline subjective image quality.

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