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.
authors.affiliation: Medical Image Computing, School of Biosciences, University of Kent, UK. rn54@kent.ac.uk
authors.forename: Nagulan
authors.initials: N
authors.lastname: Ratnarajah
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
authors.forename: Oleg
authors.initials: O
authors.lastname: Davydov
authors.forename: Ali
authors.initials: A
authors.lastname: Hojjatoleslami
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity