Abstract #3072

Comparing the performances of three diffusion kurtosis tensor estimation algorithms via a ground truth diffusion template derived from HCP data

Daniel V. Olson¹, Volkan Arpinar², and L. Tugan Muftuler²

¹Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States, ²Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States

A diffusion weighted imaging template is proposed, which is derived from the in vivo data from the HCP database. Diffusion weighted signals are generated from this template using the DKI tensor model, and diffusion tensor and kurtosis tensor metric maps are produced. These maps established the ground truth, against which the outputs of different DKI tensor estimation algorithms were compared. Rician noise is added to simulate typical diffusion MRI acquisitions with different SNR levels. The performances of the algorithms are then compared via voxel-wise Mean Square Error and bias-plus-variance decomposition to determine the optimal algorithm for the desired application.

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