Connecting and merging fibres: pathway extraction by combining probability maps.
Neuroimage
; 43(1): 81-9, 2008 Oct 15.
Article
in En
| MEDLINE
| ID: mdl-18644243
ABSTRACT
Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. A frequently suggested solution is to select curves, which pass only through two or more areas. This is very inefficient, especially for long-distance pathways and small areas. In this paper, a novel probability-based method is presented that is capable of extracting neuronal pathways defined by two seed points. A Monte Carlo simulation based tracking method, similar to the Probabilistic Index of Connectivity (PICo) approach, was extended to preserve the directional information of the main fibre bundles passing a voxel. By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Brain
/
Pattern Recognition, Automated
/
Image Interpretation, Computer-Assisted
/
Imaging, Three-Dimensional
/
Diffusion Magnetic Resonance Imaging
/
Nerve Fibers, Myelinated
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Neuroimage
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2008
Document type:
Article
Affiliation country: