An improvement to Global Tractography Using Anatomical Priors
(2014) FMS820 20141Mathematical Statistics
- Abstract (Swedish)
- Tractography is a visualization technique which reconstructs and models neural fibers in the white matter of the brain based on data from diffusion magnetic resonance imaging. It is already used locally to model
parts of dominant fiber pathways but global methods are also emerging which aim to reconstruct all the brain fibers simultaneously. In this thesis we have attempted to improve the current state of the art
of Global Tractography by introducing three principles:
* Anatomical Priors
* Introduction of fiber weights
* Reduced complexity
Our approach uses an optimization method based on Markov Chain Monte Carlo (MCMC) and Simulated annealing in order to fit a set of plausible initial fiber trajectories to a dataset acquired by... (More) - Tractography is a visualization technique which reconstructs and models neural fibers in the white matter of the brain based on data from diffusion magnetic resonance imaging. It is already used locally to model
parts of dominant fiber pathways but global methods are also emerging which aim to reconstruct all the brain fibers simultaneously. In this thesis we have attempted to improve the current state of the art
of Global Tractography by introducing three principles:
* Anatomical Priors
* Introduction of fiber weights
* Reduced complexity
Our approach uses an optimization method based on Markov Chain Monte Carlo (MCMC) and Simulated annealing in order to fit a set of plausible initial fiber trajectories to a dataset acquired by diffusion MRI. Our
method was compared to the state of the art global tractography method known as the Gibbs Tracker in a phantom study using conventional global tractography evaluation methods. In a second test, we also try the method on an in-vivo dataset of a human brain and derive the connectivity matrix with corresponding network parameters. Our approach showed considerable improvements in decreasing the amount of wrong fibers and reduced computational time. However the method still struggles to eliminate certain false but plausible connections. To remedy this, several improvements to the MCMC sampler are suggested for future work. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/4293820
- author
- Skiöldebrand, Didrik
- supervisor
- organization
- course
- FMS820 20141
- year
- 2014
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- English
- id
- 4293820
- date added to LUP
- 2014-02-12 14:30:25
- date last changed
- 2014-02-12 14:30:25
@misc{4293820,
abstract = {{Tractography is a visualization technique which reconstructs and models neural fibers in the white matter of the brain based on data from diffusion magnetic resonance imaging. It is already used locally to model
parts of dominant fiber pathways but global methods are also emerging which aim to reconstruct all the brain fibers simultaneously. In this thesis we have attempted to improve the current state of the art
of Global Tractography by introducing three principles:
* Anatomical Priors
* Introduction of fiber weights
* Reduced complexity
Our approach uses an optimization method based on Markov Chain Monte Carlo (MCMC) and Simulated annealing in order to fit a set of plausible initial fiber trajectories to a dataset acquired by diffusion MRI. Our
method was compared to the state of the art global tractography method known as the Gibbs Tracker in a phantom study using conventional global tractography evaluation methods. In a second test, we also try the method on an in-vivo dataset of a human brain and derive the connectivity matrix with corresponding network parameters. Our approach showed considerable improvements in decreasing the amount of wrong fibers and reduced computational time. However the method still struggles to eliminate certain false but plausible connections. To remedy this, several improvements to the MCMC sampler are suggested for future work.}},
author = {{Skiöldebrand, Didrik}},
language = {{eng}},
note = {{Student Paper}},
title = {{An improvement to Global Tractography Using Anatomical Priors}},
year = {{2014}},
}