DIMENSION REDUCTION BASED ON CENTROIDS FOR MULTIMODAL ANATOMICAL LANDMARK-BASED 3D/2D REGISTRATION OF CORONARY ANGIOGRAMS

We present an anatomical landmark-based rigid 3D/2D registration algorithm to register computed tomography angiography (CTA) datasets with coronary angiograms (CA) gathered during a cardiac catheterization. It has to solve for six transformation parameters (three rotation and three translation parameters). An exhaustive search in a six dimensional search space is usually computationally very expensive and algorithms using optimization strategies can get lost in local minima. We propose a method based on centroids to reduce search space from six to four dimensions. Modern C-Arm devices store a lot of information about the acquisition geometry that are used to further reduce the search space. We use this method to develop an efficient smart exhaustive search to solve for the six transformation parameters in a competitive time. With our method registration errors of < 2 mm are feasible. Execution times of < 1 sec. can be reached on a QuadCore CPU.