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

TY - JOUR

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

AU - Ratnarajah, Nagulan

AU - Simmons, Andrew

AU - Davydov, Oleg

AU - Hojjatoleslami, Ali

N1 - Copyright © 2011 Elsevier B.V. All rights reserved.

PY - 2012/1

Y1 - 2012/1

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 - Sensitivity and Specificity

KW - Computer Simulation

KW - Diffusion Tensor Imaging

KW - Image Interpretation, Computer-Assisted

KW - Reproducibility of Results

KW - Humans

KW - Algorithms

KW - Brain

KW - Models, Statistical

KW - Imaging, Three-Dimensional

KW - Data Interpretation, Statistical

KW - Image Enhancement

KW - Models, Neurological

KW - Pattern Recognition, Automated

U2 - 10.1016/j.media.2011.07.005

DO - 10.1016/j.media.2011.07.005

M3 - Article

C2 - 21917502

SN - 1361-8423

VL - 16

SP - 227

EP - 238

JO - Medical Image Analysis

JF - Medical Image Analysis

IS - 1

ER -