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Abstract #2986

Comparison of diffusion kurtosis modeling algorithms: accuracy and application

Daniel Olson 1 , Volkan Arpinar 2 , and L Tugan Muftuler 2

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

Diffusion Kurtosis Imaging (DKI) is becoming increasingly popular in diffusion weighted imaging due to its higher sensitivity to tissue microstructure compared to conventional DTI while remaining within a clinically acceptable scan time. However, the kurtosis tensor model is not as robust to noise resulting in implausible convergence of the fitting algorithm that may be mistaken as pathology. Several approaches have been proposed including outlier removal, directional weighting and regularization, and a sparsity constraint. We quantify the accuracy of each method in simulations and demonstrate performance differences with in vivo human brain data.

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