Meeting Banner
Abstract #3947

Constrained Maximum Likelihood Estimator for More Accurate Diffusion Kurtosis Tensor Estimates

Jelle Veraart1, Wim Van Hecke2,3, Dirk H. J. Poot4, Jan Sijbers1

1Vision lab, University of Antwerp, Antwerp, Belgium; 2Dept. of Radiology, University Hospitals of the Catholic University of Leuven, Leuven, Belgium; 3Dept. of Radiology, University Hospital Antwerp, Antwerp, Belgium; 4Biomedical Imaging Group Rotterdam,, Erasmus MC, Rotterdam, Netherlands

A computational framework to obtain an accurate quantification of the Gaussian and non-Gaussian component of water molecules diffusion through brain tissues with diffusion kurtosis imaging (DKI) is presented. The DKI model quantifies the kurtosis on a direction-dependent basis, constituting a higher order diffusion kurtosis tensor, which is estimated in addition to the diffusion tensor. To reconcile with the physical phenomenon of molecular diffusion, both tensor estimates should lie within a physically acceptable range. We therefore suggest to estimate both diffusional tensors by maximizing the joint likelihood function of all Rician distributed diffusion weighted images while imposing a set of constraints.