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

Ratnarajah, Nagulan, Simmons, Andy, Davydov, Oleg, Bertoni, Miguel, 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 (Unpublished) (KAR id:27767)

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 > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Q Science > QM Human anatomy
Divisions: Divisions > Division of Natural Sciences > Biosciences
Depositing User: S.A. Hojjatoleslami
Date Deposited: 12 May 2011 17:05 UTC
Last Modified: 16 Nov 2021 10:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27767 (The current URI for this page, for reference purposes)

University of Kent Author Information

Hojjatoleslami, Ali.

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