Model-Based Bootstrapping on Classified Tensor Morphologies: Estimation of Uncertainty in Fibre Orientation and Probabilistic Tractography.

Ratnarajah, Nagulan and Simmons, Andy and Davydov, Oleg and Bertoni, Miguel and Hojjatoleslami, Ali (2011) Model-Based Bootstrapping on Classified Tensor Morphologies: Estimation of Uncertainty in Fibre Orientation and Probabilistic Tractography. In: MICCAI 2011, 18-22 September 2011, Toronto Canada. (Unpublished) (Full text available)

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Abstract

In this study, fast and clinically feasible model-based bootstrapping algorithms using a geometrically constrained two-tensor diffusion model are employed for estimating uncertainty in fibre-orientation. Voxels are classified based on tensor morphologies before applying single or two-tensor model-based bootstrapping algorithms. Classification of tensor morphologies allows the tensor morphology to be considered when selecting the most appropriate bootstrap procedure. A constrained two-tensor model approach can greatly reduce data acquisition times and computational time for whole bootstrap data volume generation compared to other multi-fibre model techniques, facilitating widespread clinical use. For comparison, we propose a new repetition-bootstrap algorithm based on classified voxels and the constrained two-tensor model. White matter tractography with these bootstrapping algorithms is also developed to estimate the connection probabilities between brain regions, especially regions with complex fibre configurations. Experimental results on a hardware phantom and human brain data demonstrate the superior performance of our algorithms compared to conventional approaches.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
R Medicine > RZ Other systems of medicine
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > QM Human anatomy
Divisions: Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group
Depositing User: Sayed Ali Hojjatoleslami
Date Deposited: 12 May 2011 17:05
Last Modified: 12 May 2014 08:40
Resource URI: http://kar.kent.ac.uk/id/eprint/27767 (The current URI for this page, for reference purposes)
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