Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT

Authors:
Razvan Ioan Ionasec;Ingmar Voigt;Bogdan Georgescu;Yang Wang;Helene Houle;Joachim Hornegger;Nassir Navab;Dorin Comaniciu
Affiliations:
Integrated Data Systems, Siemens Corporate Research, Princeton, USA and Computer Aided Medical Procedures, Technical University, Munich, Germany;Software and Engineering, Siemens Corporate Technology, Erlangen, Germany and Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton, USA;Integrated Data Systems, Siemens Corporate Research, Princeton, USA;Ultrasound, Siemens Medical Solutions, Mountain View, USA;Chair of Pattern Recognition, Friedrich-Alexander-University, Erlangen, Germany;Computer Aided Medical Procedures, Technical University, Munich, Germany;Integrated Data Systems, Siemens Corporate Research, Princeton, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

The anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. An integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. In this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. A holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left heart valves. Robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional TEE and CT data. The piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. Consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. Finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. Experiments performed on 65 4D TEE and 69 4D CT sequences demonstrated an average accuracy of 1.45mm and speed of 60 seconds for the proposed approach. Initial clinical validation on model-based and expert measurement showed the precision to be in the range of the inter-user variability. To the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from TEE and CT data is proposed.