In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR). The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function. We present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. The algorithm is evaluated on rt-3DE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3DE images, each 3D mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated. Automated 4D image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluid dynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.

中文翻译：

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活体和反流二尖瓣动力学的基于图像的 4D 建模

二尖瓣动力学的体内表征依赖于从临床图像中准确重建瓣膜形态和运动的图像分析算法。此类算法的目标是提供对功能性二尖瓣和反流二尖瓣的患者特定描述，可用作生物力学分析的输入，并提供对缺血性二尖瓣反流 (IMR) 等疾病的病理生理学的深入了解。目标是生成准确的基于图像的瓣膜动力学表示，以视觉和定量的方式捕获正常和病理瓣膜功能。我们提出了一个新的框架，用于实时 3D 超声心动图 (rt-3DE) 中二尖瓣的 4D 分割和几何建模，这是一种用于二尖瓣干预术前手术计划的成像方式。该框架将分组多图集标签融合和基于模板的中间建模与卡尔曼滤波相结合，以生成定量描述和时间一致的瓣膜动力学模型。该算法根据 28 名患者的 rt-3DE 数据系列进行评估：14 名二尖瓣形态正常，14 名 IMR 严重。在这 28 个数据系列中，总共 613 个单独的 3DE 图像，每个 3D 二尖瓣分割都经过手动跟踪验证，并证明了分割之间的时间一致性。自动 4D 图像分析允许在心动周期内对二尖瓣进行可靠的非侵入性建模，以比较病理和正常二尖瓣的瓣环和瓣叶动力学。未来的研究可以将该算法应用于心血管力学应用，