Comparison of diffusion kurtosis modeling algorithms: accuracy and application
Daniel Olson 1 , Volkan Arpinar 2 , and L Tugan Muftuler 2
Biophysics, Medical College of Wisconsin,
Milwaukee, Wisconsin, United States,
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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