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Abstract #1394

Optimizing the Diffusion Weighting Gradients for Diffusion-Kurtosis Imaging

Dirk H. J. Poot1, Arjan J. den Dekker2, Marleen Verhoye3, Ines Blockx3, Johan Van Audekerke3, Annemie Van Der Linden3, Jan Sijbers1

1Visionlab, University of Antwerp, Antwerp, Belgium; 2Delft Center for Systems and Control, TUDelft, Delft, Netherlands; 3Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium

A problem in diffusion kurtosis estimation is the large number of diffusion weighted images that need to be recorded for reliable kurtosis estimates. By optimizing the individual gradient directions and strengths with which the diffusion weighted images are recorded, the precision of kurtosis measures can be maximized for any number of diffusion weighted images, or, alternatively, the number of diffusion weighted images can be minimized for a given precision. This work proposes a method to optimize the set of gradient directions and strengths of diffusion weighted images, by minimizing the Cramr-Rao-lower-bound of a kurtosis measure.