Fully Automated 4D Echocardiographic Mitral Valve Analysis for Surgical Repair

描述（由申请人提供）：二尖瓣返流（MR）影响2.4%的美国成年人[1]，即使是轻度也会增加死亡率，严重程度与生存率降低之间存在强烈的分级关系。[2，3]采用尺寸过小的瓣环成形术进行二尖瓣修复已成为功能性和退行性二尖瓣返流的首选手术治疗。然而，最近的几项长期研究记录了术后显著二尖瓣返流的意外高复发率。[5-9]改善瓣膜修复术的临床结局需要对患者特定的体内瓣膜形态进行全面的术前分析，以预测哪些患者将从瓣膜修复术中受益超过置换术，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）是在手术室检查体内瓣膜形态和功能的最实用工具。然而，目前用于检查rt-3DE图像数据的方法既效率低下，又限制了传达给手术团队的信息量。因此，本提案的目标是开发和验证一种全自动4D时空分割方法，该方法可根据rt-3DE图像数据自动生成二尖瓣的动态几何模型。假设这些模型可以准确且稳健地捕获体内二尖瓣的动态形态。为了研究这一假设，将在特定目标1中构建二尖瓣专家标记的rt-3DE图谱数据库。这些图谱对正常和患病受试者群体中的动态瓣膜形态信息进行编码，并作为4D自动分割的训练数据。在特定目标2中，将使用特定目标1中构建的参考图谱实现二尖瓣瓣叶的全自动4D分割。该算法集成了互补的概率分割和形状建模技术，自动生成4D患者特定的形状模型的二尖瓣从RT-3DE图像。在Specific Aim 3中，将使用手动图像分析作为金标准对4D分割算法进行确认。将评估方法的准确度和重现性，并优化算法以提高性能效率。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，为指导二尖瓣修复手术提供前所未有的视觉和定量数据。

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.