In this paper, an improved framework for estimation of
3-D left-ventricular deformations from tagged MRI is
presented. Contiguous short- and long-axis tagged MR
images are collected and are used within a 4-D B-Spline
based deformable model to determine 4-D
displacements and strains.
An initial 4-D B-spline model fitted to sparse
tag line data is first constructed by minimizing a
4-D Chamfer distance potential-based energy function for
isoparametric planes of the model with tag line locations;
subsequently, dense virtual tag lines
based on 2-D phase-based displacement
estimates and the initial model are created.
A final 4-D B-spline model with increased
knots is fitted to the virtual tag lines.
From the final model, we can extract accurate 3-D myocardial
deformation fields and corresponding strain maps which
are local measures of non-rigid deformation. Lagrangian
strains in simulated data are derived which show improvement
over our previous work.
The method is also applied to 3-D tagged MRI
data collected in a canine.
Segmentation and measurement of vascular structures is an important topic in medical image analysis. The aim of this paper is to present a hybrid approach to accurate segmentation of vascular structures from MRA images using level set methods and deformable geometric model, constructed with 3D Delaunay triangulation. Based on the analysis of local intensity structure, multiple scale filtering is derived from the Hessian matrix and then is used to effectively enhance vessel structures with various diameters. We apply the level set method to automatically segment vessels enhanced by the filtering. The segmentation of vessels from 3D vessel enhanced images can be regarded as an evolution of a propagating implicit surface in a 3D space that separates vessel volumes from another and moves in a normal direction to the vessel boundaries with a given speed function over time. The speed function used is derived from the results of filtering. In subsequent step, in order to make the segmented vessel surface fit the actual vessel surface more accurately, we triangulate the segmented vessel surface using 3D Delaunay triangulation and use the triangulated surface as a deformable model in order to minimize an energy functional in which the internal force is defined as linear equations with the local surface patch given by 3D Delaunay triangulation and the external force is derived from the gradient information of original images. Using the proposed method, vessels can be effectively and accurately segmented from MRA images.