Non-rigid coregistration of diffusion kurtosis data

Authors:
J. Veraart;W. Van Hecke;I. Blockx;A. Van Der Linden;M. Verhoye;J. Sijbers
Affiliations:
Visionlab, Department of Physics, University of Antwerp, Antwerp, Belgium;Department of Radiology, Antwerp University Hospital, Antwerp, Belgium and Department of Radiology, University Hospitals of the Catholic University of Leuven, Leuven, Belgium;Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium;Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium;Bio-Imaging Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium;Visionlab, Department of Physics, University of Antwerp, Antwerp, Belgium
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Diffusion kurtosis imaging (DKI) is a relatively new model to study the non-Gaussian behavior of water diffusion in the brain white matter which introduces, besides the conventional diffusion tensor, a 4th order, 3D diffusion kurtosis tensor to describe the diffusion. In this study, a multi-component coregistration algorithm using a viscous fluid model and mutual infonnation is optimized to enable more accurate alignment of the higher order tensor DKI data. The preservation of principle strategy is extended in order to facilitate tensor reorientation of the diffusion and diffusion kurtosis tensors. In addition, experiments demonstrated that involving kurtosis information in the coregistration procedure significantly improves tensor alignment.