Fully Automated 4D Echocardiographic Mitral Valve Analysis for Surgical Repair

描述(申请人提供)：二尖瓣返流(MR)影响2.4%的美国成年人[1]，即使在轻微的情况下也会增加死亡率，严重程度和生存率之间存在强烈的分级关系。[2，3]二尖瓣环状成形术已成为功能性和退行性二尖瓣返流的首选外科治疗方法。然而，最近的几项长期研究记录了手术后显著的MR复发率出乎意料地高。[5-9]要改善瓣膜修复的临床结果，需要对患者特定的活体瓣膜形态进行全面的术前分析，预测哪些患者将从瓣膜修复中受益，而不是替换，并确定针对患者特定瓣膜几何形状扭曲的修复策略。实时三维超声心动图(RT-3DE)是目前最实用的活体瓣膜形态和功能检查工具。然而，目前检查RT-3DE图像数据的方法效率低下，而且传递给手术团队的信息量有限。因此，该方案的目标是开发和验证一种全自动的4D时空分割方法，该方法可以根据RT-3DE图像数据自动生成二尖瓣的动态几何模型。假设这些模型能够准确、稳健地捕捉到活体二尖瓣的动态形态。为了验证这一假设，我们将在特定的目标1中建立二尖瓣RT-3DE图谱的专家标记数据库。这些图谱编码关于正常和疾病受试者群体中的动态瓣膜形态的信息，并作为4D自动分割的训练数据。在特定目标2中，将使用在特定目标1中构建的参考图谱来实现二尖瓣叶的全自动4D分割。该算法结合互补的概率分割和形状建模技术，从RT-3DE图像中自动生成针对患者的4D二尖瓣形状模型。在具体目标3中，将使用人工图像分析作为金标准来验证4D分割算法。将对该方法的准确性和重复性进行评估，并将针对性能效率对算法进行优化。该项目成功完成后，外科医生将拥有一种有效的术前瓣膜评估工具，将为指导二尖瓣修复手术提供前所未有的可视化和定量数据。

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.