Monocular Deformable Model-to-Image Registration of Vascular Structures

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, our method does not require a segmentation of 2D vasculature. In consequence, 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.

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.