Search

Vascular disease produces changes in lumenal shape evident in magnetic resonance angiography (MRA). However, quantification of vascular shape from MRA is problematic due to image artifacts. Prior deformable models for vascular surface reconstruction primarily resolve problems of initialization of the surface mesh. However, initialization can be obtained in a trivial manner for MRA using isosurfaces. We propose a methodology for deforming the isosurface to conform to the boundaries of objects in the image with minimal a priori assumptions of object shape. As in conventional methods, external forces attract the surface toward edges in the image. However, smoothing is produced by a moment that aligns the normals of adjacent surface triangles. Notably, the moment produces no translational motion of surface triangles. The deformable isosurface was applied to a digital phantom of a stenotic artery, to MRA of three renal arteries with atherosclerotic disease and MRA of one carotid artery with atherosclerotic disease. Results of the surface reconstruction from the deformable model were compared with conventional X-ray angiography for the renal arteries. Measurement of the degree of stenosis of the renal arteries was within 12% [+ or -] 6%. The deformable model provided improvements over the isosurface in all cases in terms of measurement of the degree of stenosis or improving the surface smoothness. Index Terms--Carotid artery, deformable model, magnetic resonance angiography, renal artery.

Three-dimensional (3-D) angiographic methods are gaining acceptance for evaluation of atherosclerotic disease. However, measurement of vessel stenosis from 3-D angiographic methods can be problematic due to limited image resolution and contrast. We present a method for reconstructing vessel surfaces from 3-D angiographic methods that allows for objective measurement of vessel stenosis. The method is a deformable model that employs a tubular coordinate system. Vertex merging is incorporated into the coordinate system to maintain even vertex spacing and to avoid problems of self-intersection of the surface. The deformable model was evaluated on clinical magnetic resonance (MR) images of the carotid (n = 6) and renal (n = 2) arteries, on an MR image of a physical vascular phantom and on a digital vascular phantom. Only one gross error occurred for all clinical images. All reconstructed surfaces had a realistic, smooth appearance. For all segments of the physical vascular phantom, vessel radii from the surface reconstruction had an error of less than 0.2 of the average voxel dimension. Variability of manual initialization of the deformable model had negligible effect on the measurement of the degree of stenosis of the digital vascular phantom. Index Terms--Atherosclerosis, deformable model, magnetic resonance angiography, surface reconstruction.

Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm. Index Terms--Magnetic resonance angiography, skeletonization, visualization.

This paper presents a novel automatic 3D hybrid segmentation approach based on free-form deformation. The algorithms incorporate boosting and deformation gradients to achieve reliable liver segmentation of Computed Tomography (CT) scans. A free-form deformable model is deformed under the forces originating from boosting and deformation gradients. The basic idea of the scheme is to combine information from intensity and shape prior knowledge to calculate desired displacements to the liver boundary on vertices of deformable surface. Boosting classifies the 3D image into a binary mask and the edgeflow generates a force field from the mask. The deformable surface deforms iteratively according to the force field. Deformation gradients cast restriction at each deformation step. The deformation converges to a stable status to achieve the final segmentation surface.