A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves

N. Ratnarajah, A. Simmons, Oleg Davydov, Ali Hojjatoleslami

Research output: Contribution to journalArticle

3 Citations (Scopus)
149 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)227-238
Number of pages12
JournalMedical Image Analysis
Volume16
DOIs
Publication statusPublished - 2012

Keywords

  • probabilistic fibre tracking
  • tractography
  • probabilistic fibre tracking curves

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