Key result
The proposed automated modeling algorithm facilitated extraction of geometric vessel-tree models from 3D MR angiography images and outperformed the Hessian vector approach in noisy images.
The proposed algorithm improves the automated extraction and modeling of tubular blood vessels from 3D MR angiography images compared to the traditional Hessian vector approach.
May enhance vessel extraction in noisy MR angiography; leaves open prospective clinical validation before adoption.
An algorithm is developed for automated modeling of tubular blood vessel segments, based on their noisy 3D raster image. The approach is based on continuous-function approximation of binary skeleton lines extracted from thresholded multiscale vesselness images. The continuous centerline functions allow robust computation of tangent vectors, to define normal planes and 3D image cross-sections on those planes. A vessel intensity profile model is next least-squares fitted to the image cross-section along straight lines segments - anchored at centerline and extended toward vessel walls, at a number of directions covering the full angle. Vessel parameters, such as local radius for circular vessels, distances between the centerline and edges for non-circular shapes or intensity profile corresponding to blood velocity distribution, are estimated through the model fitting. Subvoxel accuracy vessel representation, robustness to noise and image inhomogeneity are of primary concern. The algorithm is applied to 3D synthetic and real-life magnetic resonance images. It is demonstrated that the proposed method facilitates automated extraction of geometric vessel-tree models from images and outperforms the well-known Hessian vector approach in terms of accurate estimation of the centerline local direction in noisy images.
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Materka et al. (2015) studied Tubular blood vessel modeling in 3D MR angiography. Automated modeling algorithm based on continuous-function approximation vs. Hessian vector approach was evaluated on Accurate estimation of the centerline local direction in noisy images. The proposed automated modeling algorithm facilitated extraction of geometric vessel-tree models from 3D MR angiography images and outperformed the Hessian vector approach in noisy images.
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