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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Razvan Ioan Ionasec , Ingmar Voigt , Bogdan Georgescu , Yang Wang , Helene Houle , Joachim Hornegger , Nassir Navab , Dorin Comaniciu

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. More... »

PAGES

767-775

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-04271-3_93

DOI

http://dx.doi.org/10.1007/978-3-642-04271-3_93

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035476151

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20426181


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