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Abstract #1044

Robust DKI parameter estimation in case of CSF partial volume effects

Quinten Collier1, Arnold Jan den Dekker1,2, 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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