Abstract
As decisions in cardiology increasingly rely on non-invasive methods, fast and precise image analysis tools have become a crucial component of the clinical workflow. Especially when dealing with complex cardiovascular disorders, such as valvular heart disease, advanced imaging techniques have the potential to significantly improve treatment outcome as well as to reduce procedure risks and related costs. We are developing patient-specific cardiac models, estimated from available multi-modal images, to enable advanced clinical applications for the management of cardiovascular disease. In particular, a novel physiological model of the complete heart, including the chambers and valvular apparatus is introduced, which captures a large spectrum of morphological, dynamic and pathological variations. To estimate the patient-specific model parameters from four-dimensional cardiac images, we have developed a robust learning-based framework. The model-driven approach enables a multitude of advanced clinical applications. Gold standard clinical methods, which manually process 2D images, can be replaced with fast, precise, and comprehensive model-based quantification to enhance cardiac analysis. For emerging percutaneous and minimal invasive valve interventions, cardiac surgeons and interventional cardiologists can substantially benefit from automated patient selection and virtual valve implantation techniques. Furthermore, the complete cardiac model enables for patient-specific hemodynamic simulations and blood flow analysis. Extensive experiments demonstrated the potential of these technologies to improve treatment of cardiovascular disease.
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Ionasec, R. et al. (2010). Patient-Specific Modeling of the Heart: Applications to Cardiovascular Disease Management. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. STACOM 2010. Lecture Notes in Computer Science, vol 6364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15835-3_2
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DOI: https://doi.org/10.1007/978-3-642-15835-3_2
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