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

Ratnarajah, Nagulan, Simmons, Andy, Hojjatoleslami, Ali, Davydov, Oleg (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. (doi:10.1016/j.media.2011.07.005) (KAR id:27764)

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
DOI/Identification number: 10.1016/j.media.2011.07.005
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 > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Divisions > Division of Natural Sciences > Biosciences
Depositing User: S.A. Hojjatoleslami
Date Deposited: 12 May 2011 17:24 UTC
Last Modified: 16 Nov 2021 10:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/27764 (The current URI for this page, for reference purposes)

University of Kent Author Information

Hojjatoleslami, Ali.

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