4D Multimodal Image-Based Modeling for Bicuspid Aortic Valve Repair Surgery

二尖瓣修复术是治疗青少年主动脉瓣关闭不全的一种很有前途的外科治疗方法。 (AR)。然而，BAV修复手术仍然没有得到充分利用，而且在各机构中的应用情况各不相同，这是因为 部分原因是缺乏BAV维修计划的标准化方法。目前，BAV维修计划依赖于 主要是术中通过直接观察对瓣膜进行手动测量，而心脏 处于停搏状态，使得外科医生很难在生理状态下识别瓣膜动力学缺陷 条件。为了应对这一挑战，长期目标是开发多模式4D图像分析和 瓣膜建模平台，系统地描述了术前BAV的形态和动力学特征，以及 实现针对患者的手术计划。这项建议的总体目标是：(一)充实一门知识 构成BAV的主动脉瓣、环和根部之间的精确解剖关系的间隙 以及(Ii)开发计算机图像分析以准确识别患者- 导致AR的特定、解剖和动态扭曲，因此可以优先考虑这些缺陷的风险 BAV修复术的分层与规划。这项工作将通过追求三个具体目标来进行： (1)设计并评估了一种适用于BAV4D重建的自动分割和建模算法 来自多种临床影像模式的仪器；(2)表征形态和动态特征 并创建了一种用于全面异常检测的机器学习方法 反流性BAV；(3)使用瓣膜修复获得的图像评估BAV修复计划系统 三个机构的程序。拟议的项目利用了两个项目的互补优势 方式：实时3D经食道超声心动图和4D计算机断层血管成像， 它以高空间分辨率捕捉到了主动脉瓣的形态细节和血管的运动 具有高时间分辨率的3D BAV设备。该项目的创新之处在于，提出的工具 可能会改变BAV维修计划的实施方式。而不是依靠术中检查 当瓣膜未加压时，外科医生可以交互地可视化由图像导出的BAV模型并量化 术前瓣膜处于4D生理状态时AR的动力学机制。的重要意义。 这项研究是，它可以促进跨机构的瓣膜修复计划的一致性，减少 外科医生对直觉和试错法的依赖，从而提高青少年BAV修复术的利用率 成年人。与传统的瓣膜替换相比，这将具有生活质量优势，这需要 终身抗凝治疗(机械瓣膜)或因耐用性有限而多次更换 (生物瓣膜)。最后，对计算机辅助阀门的多模式图像数据进行了系统分析 缺陷检测将在很大程度上有利于后天和先天性心脏外科治疗的进步 疾病。

Bicuspid aortic valve (BAV) repair is a promising surgical treatment for young adults with aortic regurgitation (AR). However, BAV repair surgery remains underutilized and variably applied across institutions, owing in part to the lack of a standardized approach to BAV repair planning. Currently, BAV repair planning relies primarily on intraoperative manual measurements of the valve made by direct observation while the heart is in an arrested state, making it difficult for the surgeon to identify defects in valve dynamics under physiological conditions. To address this challenge, the long-term goal is to develop a multimodal 4D image analytics and valve modeling platform that systematically characterizes pre-operative BAV morphology and dynamics and enables patient-specific surgical planning. The overall objectives of this proposal are to (i) fill a knowledge gap in the precise anatomical relationships between the aortic cusps, annulus, and root that make a BAV functionally competent, and (ii) develop computational image analytics to precisely identify the patient- specific, anatomical and dynamic distortions that cause AR so that these defects can be prioritized for risk stratification and planning of BAV repair surgery. This work will be carried out by pursuing three specific aims: (1) Design and assess an automated segmentation and modeling algorithm for 4D reconstruction of the BAV apparatus from multiple clinical imaging modalities; (2) characterize the morphological and dynamic features of BAV competence and create a machine learning method for comprehensive anomaly detection in regurgitant BAVs; (3) evaluate a BAV repair planning system using images acquired from valve repair procedures at three institutions. The proposed project leverages the complementary benefits of two modalities: real-time 3D transesophageal echocardiography and 4D computed tomography angiography, which capture both the morphological detail of the aortic cusps with high spatial resolution and the motion of the 3D BAV apparatus with high temporal resolution. The innovation of this project is that the proposed tools could change how BAV repair planning is carried out. Instead of relying on intraoperative inspection of the valve while it is unpressurized, the surgeon can interactively visualize image-derived BAV models and quantify dynamic mechanisms of AR when the valve is in a pre-operative 4D physiological state. The significance of this research is that it could promote consistency in valve repair planning across institutions, decrease surgeons’ reliance on intuition and trial-and-error, and thereby increase the utilization of BAV repair in young adults. This would have quality of life advantages relative to conventional valve replacement, which requires lifelong anticoagulation therapy (mechanical valves) or multiple re-replacements due to limited durability (bioprosthetic valves). Ultimately, the systematic analysis of multimodal image data for computer-aided valve defect detection will broadly benefit advancement of surgical treatments for acquired and congenital heart disease.