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Abstract #1344

Optimal Diffusion Sampling Scheme for High Performance Gradients

Nastaren Abad1, Luca Marinelli1, Radhika Madhavan1, James Kevin DeMarco2, Robert Y Shih2,3, Vincent B Ho2,3, Gail Kohls2, and Tom K.F Foo1
1General Electric Global Research, Niskayuna, NY, United States, 2Walter Reed National Military Medical Center, Bethesda, MD, United States, 3Uniformed Services University of the Health Sciences, Bethesda, MD, United States

In order to establish a benchmark for future studies, we utilize a data driven approach towards optimizing diffusion sampling for an ultra-high-performance gradient MRI sub-system as a means to establish minimal discrepancy compared to a fully sampled, defined superset. This study focused on b-value contribution and the impact of decreased sampling on uncertainty of diffusion and kurtosis tensor estimates, and fiber orientation to resolve sub-voxel information.

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