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)

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.
LanguageEnglish
Pages227-238
Number of pages12
JournalMedical Image Analysis
Volume16
DOIs
Publication statusPublished - 2012

Fingerprint

Fibers
Chemical analysis
Seed
Seeds
Clustering algorithms
Probability distributions
Tensors
Cluster Analysis
Trajectories

Keywords

  • probabilistic fibre tracking
  • tractography
  • probabilistic fibre tracking curves

Cite this

Ratnarajah, N. ; Simmons, A. ; Davydov, Oleg ; Hojjatoleslami, Ali. / A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves. In: Medical Image Analysis. 2012 ; Vol. 16. pp. 227-238.
@article{77aa0b21fafd48aba2e81875ada0fc2e,
title = "A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves",
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.",
keywords = "probabilistic fibre tracking, tractography , probabilistic fibre tracking curves",
author = "N. Ratnarajah and A. Simmons and Oleg Davydov and Ali Hojjatoleslami",
year = "2012",
doi = "10.1016/j.media.2011.07.005",
language = "English",
volume = "16",
pages = "227--238",
journal = "Medical Image Analysis",
issn = "1361-8415",

}

A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves. / Ratnarajah, N.; Simmons, A.; Davydov, Oleg; Hojjatoleslami, Ali.

In: Medical Image Analysis, Vol. 16, 2012, p. 227-238.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A novel approach for improved tractography and quantitative analysis of probabilistic fibre tracking curves

AU - Ratnarajah, N.

AU - Simmons, A.

AU - Davydov, Oleg

AU - Hojjatoleslami, Ali

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - probabilistic fibre tracking

KW - tractography

KW - probabilistic fibre tracking curves

UR - http://www.scopus.com/inward/record.url?scp=82355175868&partnerID=8YFLogxK

U2 - 10.1016/j.media.2011.07.005

DO - 10.1016/j.media.2011.07.005

M3 - Article

VL - 16

SP - 227

EP - 238

JO - Medical Image Analysis

T2 - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

ER -