PURPOSE Diffusion kurtosis imaging (DKI) is a recent improvement over diffusion tensor imaging that characterizes tissue by quantifying non-gaussian diffusion using a 3D fourth-order kurtosis tensor. DKI needs to consider three constraints to be physically relevant. Further, it can be improved by considering the Rician signal noise model. A DKI estimation method is proposed that considers all t...

Independent component analysis (ICA) is a computational mehtod to solve blind source separation (BSS) problem. Different kinds of classic measure can be used for the estimation of nonGaussian sources by ICA. In this paper we review independent componenet analysis (ICA) technique based on Kurtosis contrast function. We briefly present the common independent component analysis algorithms that use...

Introduction Diffusion kurtosis imaging is a new method to estimate the non gaussianity of the diffusion process with diffusion weighted MR images (DWIs) [1]. The model of the DWIs suggested in [1] was based on the Taylor series expansion of the logarithm of the DW magnitude. Unfortunately, this Taylor approximation is only valid for small b-values and leads to diverging predicted DW magnitudes...

Enhanced tissue classification of acute ischemic diffusion kurtosis lesion with intrinsic kurtosis heterogeneity correction Phillip Zhe Sun, Yu Wang, Emiri Mandeville, Mark Vangel, Eng Lo, and Xunming Ji Radiology, Martinos Center for Biomedical Imaging, Charlestown, MA, United States, Neuroprotection Research Laboratory, Department of Radiology and Neurology, Massachusetts General Hospital, Ha...