Patient specific models for planning and guidance of minimally invasive aortic valve implantation.

Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In addition, we present a new method for the detection of the coronary ostia that exploits the model-based segmentation and show, how a number of clinical measurements such as diameters and the distances between aortic valve plane and coronary ostia can be derived that are important for procedure planning. Validation results are based on accurate reference annotations of 20 CT images from different patients and leave-one-out tests. They show that model adaptation can be done with a mean surface-to-surface error of 0.5mm. For coronary ostia detection a success rate of 97.5% is achieved. Depending on the measured quantity, the segmentation translates into a root-mean-square error between 0.4 - 1.2mm when comparing clinical measurements derived from automatic segmentation and from reference annotations.