Finsler Streamline Tracking with Single Tensor Orientation Distribution Function for High Angular Resolution Diffusion ImagingReportar como inadecuado




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Journal of Mathematical Imaging and Vision

, Volume 41, Issue 3, pp 170–181

First Online: 11 February 2011

Abstract

We introduce a new framework based on Riemann-Finsler geometry for the analysis of 3D images with spherical codomain, more precisely, for which each voxel contains a set of directional measurements represented as samples on the unit sphere antipodal points identified. The application we consider here is in medical imaging, notably in High Angular Resolution Diffusion Imaging HARDI, but the methods are general and can be applied also in other contexts, such as material science, et cetera, whenever direction dependent quantities are relevant. Finding neural axons in human brain white matter is of significant importance in understanding human neurophysiology, and the possibility to extract them from a HARDI image has a potentially major impact on clinical practice, such as in neuronavigation, deep brain stimulation, et cetera. In this paper we introduce a novel fiber tracking method which is a generalization of the streamline tracking used extensively in Diffusion Tensor Imaging DTI. This method is capable of finding intersecting fibers in voxels with complex diffusion profiles, and does not involve solving extrema of these profiles. We also introduce a single tensor representation for the orientation distribution function ODF to model the probability that a vector corresponds to a tangent of a fiber. The single tensor representation is chosen because it allows a natural choice of Finsler norm as well as regularization via the Laplace-Beltrami operator. In addition we define a new connectivity measure for HARDI-curves to filter the most prominent fiber candidates. We show some very promising results on both synthetic and real data.

KeywordsODF DTI HARDI Riemann geometry Finsler geometry Fiber tracking  Download to read the full article text



Autor: Laura Astola - Andrei Jalba - Evgeniya Balmashnova - Luc Florack

Fuente: https://link.springer.com/







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