ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Fast Virtual Stenting with Deformable Meshes: Application to Intracranial Aneurysms
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Patient-specific modeling of the heart: applications to cardiovascular disease management
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
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Valve replacement is the most common therapy for diseased aortic valves. Percutaneous approaches are becoming increasingly popular, due to reduced procedural complications and lower follow-up rates. Still there is a lack of efficient tools for valve quantification and preoperative simulation of replacement and repair procedures. Thus the success of the intervention relies to a large portion on experience and skills of the operator. In this paper we propose a novel framework for preoperative planning, intraoperative guidance and post-operative assessment of percutaneous aortic valve replacement procedures with stent mounted devices. A comprehensive model of the aortic valvular complex including aortic valve and aorta ascendens is estimated with fast and robust learning-based techniques from cardiac CT images. Consequently our model is used to perform a in-silico delivery of the valve implant based on deformable simplex meshes and geometrical constraints. The predictive power of the model-based in-silico valve replacement was validated on 3D cardiac CT data from 20 patients through comparison of preoperative prediction against postoperatively imaged real device. In our experiments the method performed with an average accuracy of 2.18 mm and a speed of 55 seconds. To the best of our knowledge, this is the first time a computational framework is validated using real pre- and postoperative patient data.