The registration of 3D vasculature to 2D projections is the key for providing advanced systems for image-based navigation and guidance. In areas with non-rigid patient motion, however, it is very difficult to accurately perform the registration if only one 2D view is available. We propose a method for deformable registration of a 3D vascular model extracted from an angiographic scan to a single 2D Digitally Subtracted Angiogram (DSA). Different to existing approaches, we avoid a segmentation of 2D vasculature. Thus, our method can be used without manual interaction during medical treatment. Formulated as an energy minimization problem our approach combines a novel data term with the length regularization proposed in [1] which removes the ill-posedness of this monocular scenario. Besides attracting projected 3D centerline points to locations with high vessel probability the proposed data term ensures an injective projection of the centerline points. As a consequence our cost function ensures length preservation in centerline direction as well as orthogonal to it. Moreover, due to our novel image-based data term, we achieve a considerable gain in performance compared to feature-based approaches. Accuracy, robustness to outliers, as well as performance issues are analyzed through tests on synthetic and real data within a controlled environment.
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The registration of 3D vasculature to 2D projections is the key for providing advanced systems for image-based navigation and guidance. In areas with non-rigid patient motion, however, it is very difficult to accurately perform the registration if only one 2D view is available. We propose a method for deformable registration of a 3D vascular model extracted from an angiographic scan to a single 2D Digitally Subtracted Angiogram (DSA). Different to existing approaches, we avoid a segmentation of...
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