Novel Approach for Improved Tractography and Quantitative Analysis of Probabilistic Fibre Tracking Curves

Ratnarajah, N. and Simmons, A. and Hojjatoleslami, A. and Davydov, O. (2012) Novel Approach for Improved Tractography and Quantitative Analysis of Probabilistic Fibre Tracking Curves. Medical Image Analysis, 16 (1). pp. 227-238. ISSN 1361-8415. (Full text available)

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http://dx.doi.org/10.1016/j.media.2011.07.005

Abstract

This paper presents a novel approach for improved diffusion tensor fibre tractography, aiming to tackle a number of the limitations of current fibre tracking algorithms, and describes a quantitative analysis tool for probabilistic tracking algorithms. We consider the sampled random paths generated by a probabilistic tractography algorithm from a seed point as a set of curves, and develop a statistical framework for analysing the curve-set geometrically that finds the average curve and dispersion measures of the curve-set statistically. This study is motivated firstly by the goal of developing a robust fibre tracking algorithm, combining the power of both deterministic and probabilistic tracking methods using average curves. These typical curves produce strong connections to every anatomically distinct fibre tract from a seed point and also convey important information about the underlying probability distribution. These single well-defined trajectories overcome a number of the limitations of deterministic and probabilistic approaches. A new clustering algorithm for branching curves is employed to separate fibres into branches before applying the averaging methods. Secondly, a quantitative analysis tool for probabilistic tracking methods is introduced using statistical measures of curve-sets. Results on phantom and in vivo data confirm the efficiency and effectiveness of the proposed approach for the tracking algorithm and the quantitative analysis of the probabilistic methods.

Item Type: Article
Subjects: Q Science > Q Science (General)
R Medicine > R Medicine (General)
Q Science > QA Mathematics (inc Computing science) > QA440 Geometry > QA611 Topology
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculties > Science Technology and Medical Studies > School of Computing
Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Faculties > Science Technology and Medical Studies > School of Biosciences > Biomedical Research Group
Depositing User: Sayed Ali Hojjatoleslami
Date Deposited: 12 May 2011 17:24
Last Modified: 10 Jan 2012 11:40
Resource URI: http://kar.kent.ac.uk/id/eprint/27764 (The current URI for this page, for reference purposes)
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