Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Model-Driven Quantitative and Visual Evaluation of the Aortic Valve from 4D CT
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Model-based fusion of multi-modal volumetric images: application to transcatheter valve procedures
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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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.