Location of Repository

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

By Nagulan Ratnarajah, Andy Simmons, Ali Hojjatoleslami and Oleg Davydov

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

Topics: Q1, R1, QA611, QA273, RC0321
Publisher: Elsevier
Year: 2012
OAI identifier: oai:kar.kent.ac.uk:27764

Suggested articles

Preview

Citations

  1. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. doi
  2. (2003). Characterization and propagation of uncertainty in diusion-weighted MR imaging. doi
  3. (2004). Clustering ber tracts using normalized cuts. In: doi
  4. (2004). Clustering fiber tracts using normalized cuts. In: doi
  5. (2000). In vivo tractography using DT-MRI data. doi
  6. (2000). In vivo fiber tractography using DT-MRI data. doi
  7. (1997). New histological and physiological stains derived from diffusiontensor MR images, doi
  8. (1997). New histological and physiological stains derived from diusiontensor MR images, doi
  9. (2008). Probabilistic streamline q-ball tractography using the residual bootstrap. doi
  10. (2006). Quanti of the shape of tracts. doi
  11. (2006). Quantification of the shape of fiber tracts. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.