Abstract #1344

Optimal Diffusion Sampling Scheme for High Performance Gradients

Nastaren Abad¹, Luca Marinelli¹, Radhika Madhavan¹, James Kevin DeMarco², Robert Y Shih^2,3, Vincent B Ho^2,3, 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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