Abstract #1044

Robust DKI parameter estimation in case of CSF partial volume effects

Quinten Collier¹, Arnold Jan den Dekker^1,2, Ben Jeurissen¹, and Jan Sijbers¹

¹iMinds Vision Lab, University of Antwerp, Antwerp, Belgium, ²Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands

Diffusion kurtosis imaging (DKI) suffers from partial volume effects caused by cerebrospinal fluid (CSF). We propose a DKI+CSF model combined with a framework to robustly estimate the DKI parameters. Since the estimation problem is ill-conditioned, a Bayesian estimation approach with a shrinkage prior is incorporated. Both simulation and real data experiments suggest that the use of this prior leads to a more accurate, precise and robust estimation of the DKI+CSF model parameters. Finally, we show that not correcting for the CSF compartment can lead to severe biases in the parameter estimations.

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