Aortic and Mitral Valve Modeling From Multi-Modal Image Data

Abstract The cardiac valvular apparatus is an essential part of the anatomical, functional, and hemodynamic characteristics of the heart and the cardiovascular system. Valvular heart diseases often involve multiple dysfunctions and require joint assessment and therapy. In this chapter, we introduce a modular patient-specific model of the aortic and mitral valvular apparatus. We present a discriminative learning-based framework that permits the estimation of patient-specific model parameters from multi-modal cardiac images. In addition, we introduce a shape representation, the ShapeForest, which is able to model complex shape variation, preserves local shape information, and incorporates prior knowledge during shape space inference. From the patient-specific model a wide range of clinical biomarkers can be derived during functional assessment and intervention planning. Experiments on cardiac computed tomography and transesophageal echocardiogram studies demonstrate the performance and clinical potential of the proposed method. Our method enables automatic quantitative evaluation of the left heart valvular apparatus based on noninvasive imaging techniques.