Volumetric Shape Model for Oriented Tubular Structure from DTI Data

Abstract

In this paper, we describe methods for constructing shape priors using orientation information to model white matter tracts from magnetic resonance diffusion tensor images (DTI). Shape Normalization is needed for the construction of a shape prior using statistical methods. Moving beyond shape normalization using boundary-only or orientation-only information, our method combines the idea of sweeping and inverse-skeletonization to parameterize 3D volumetric shape, which provides point correspondence and orientations over the whole volume in a continuous fashion. Tangents from this continuous model can be treated as a de-noised reconstruction of the original structural orientation inside a shape. We demonstrate the accuracy of this technique by reconstructing synthetic data and the 3D cingulum tract from brain DTI data and manually drawn 2D contours for each tract. Our output can also serve as the input for subsequent boundary finding or shape analysis.

Fasciculography: Robust Prior-Free Real-Time Normalized Volumetric Neural Tract Parcellation

“…Our goal is to find a parametric volume V (α, β, γ,): ℝ 3 → ℝ 3 where α, β, γ, ∈ [0, 2π] and α, γ are symmetric, which best represents an oriented structure in a vector field [15]. The model V (α, β, γ) defines the coordinate system for the tract volume, indexing all points in the volume, facilitating (as described below) tract comparison and the use of the Fourier basis for filtering.…”

“…Various Tract-based Morphometry (TBM) methods [10], [11], [12] tackle the point correspondence issue mainly by generating a common parametrization through post-processing and registering fibers. The conversion from fibers to a common coordinate system can be avoided by using a tract-shape [13], [14], [15] directly for parcellation. Nevertheless, streamline tracking and bundling can be a convenient way to visualize a vector field which represents anatomic structure.…”