Fully Automated 4D Echocardiographic Mitral Valve Analysis for Surgical Repair

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DESCRIPTION (provided by applicant): Mitral regurgitation (MR) affects 2.4% of adult Americans [1] and increases mortality even when mild, with a strongly-graded relationship between severity and reduced survival.[2,3] Mitral valve repair with undersized ring annuloplasty has become the preferred surgical treatment for both functional and degenerative MR. However, several recent long-term studies have documented unexpectedly high recurrence rates of significant MR after surgery.[5-9] Improving the clinical outcome of valve repair requires a thorough pre-operative analysis of patient-specific in vivo valve morphology, to predict which patients will benefit from valve repair over replacement and to identify repair strategies that target patient-specific distortions in valve geometry. Real-time 3D echocardiography (rt-3DE) is the most practical tool for inspection of in vivo valve morphology and function in the operating room. However, the current methods for examining rt-3DE image data are both inefficient and limited in the amount of information conveyed to the surgical team. Therefore, the goal of this proposal is to develop and validate a fully automated 4D spatiotemporal segmentation method that automatically generates dynamic geometric models of the mitral valve from rt-3DE image data. It is hypothesized that these models can accurately and robustly capture the dynamic morphology of the in vivo mitral valve. To investigate this hypothesis, a database of expert-labeled rt-3DE atlases of the mitral valve will be constructed in Specific Aim 1. These atlases encode information about dynamic valve morphology in a population of normal and diseased subjects and serve as training data for 4D automatic segmentation. In Specific Aim 2, a fully automatic 4D segmentation of the mitral leaflets will be implemented using the reference atlases constructed in Specific Aim 1. The proposed algorithm integrates complementary probabilistic segmentation and shape modeling techniques to automatically generate 4D patient-specific shape models of the mitral valve from rt-3DE images. In Specific Aim 3, the 4D segmentation algorithm will be validated using manual image analysis as a gold standard. The accuracy and reproducibility of the method will be assessed, and the algorithm will be optimized for performance efficiency. Upon successful completion of this project, surgeons will have an efficient tool for pre-operative valve assessment that will provide unprecedented visual and quantitative data for guidance of mitral valve repair surgery.