Title
Non-rigid coregistration of diffusion kurtosis data
Author
Faculty/Department
Faculty of Sciences. Physics
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
conferenceObject
Publication
New York, N.Y. :IEEE, [*]
Subject
Physics
Computer. Automation
Source (book)
ISBI'10: proceedings of the 2010 IEEE International Conference on Biomedical Imaging: From Nano to Macro, Rotterdam, 14-17 April
ISBN - Hoofdstuk
978-1-4244-4125-9
ISI
000287997400101
Carrier
E
Target language
Dutch (dut)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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 information 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.
E-info
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Handle