We propose a novel registration method, which combines well-known vessel detection techniques with aspects of
model adaptation. The proposed method is tailored to the requirements of 2D-3D-registration of interventional
angiographic X-ray data such as acquired during abdominal procedures. As prerequisite, a vessel centerline is
extracted out of a rotational angiography (3DRA) data set to build an individual model of the vascular tree.
Following the two steps of local vessel detection and model transformation the centerline model is matched to one
dynamic subtraction angiography (DSA) target image. Thereby, the in-plane position and the 3D orientation
of the centerline is related to the vessel candidates found in the target image minimizing the residual error in
least squares manner. In contrast to feature-based methods, no segmentation of the vessel tree in the 2D target
image is required. First experiments with synthetic angiographies and clinical data sets indicate that matching
with the proposed model-to-image based registration approach is accurate and robust and is characterized by a
large capture range.