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

二叶式主动脉瓣（BAV）修复术是治疗年轻成人主动脉瓣关闭不全的一种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，并且在各机构中应用不多， 部分原因是缺乏BAV修复计划的标准化方法。目前，BAV修复计划依赖于 主要是通过直接观察瓣膜的术中手动测量，而心脏 这使得外科医生难以在生理条件下识别瓣膜动力学的缺陷， 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， 系统表征术前BAV形态和动力学的瓣膜建模平台， 实现针对患者的手术计划。本提案的总体目标是（一）填补知识 构成BAV的主动脉瓣尖、瓣环和根部之间精确解剖关系的间隙 功能胜任，和（ii）开发计算图像分析，以精确识别患者- 导致AR的特定、解剖学和动态扭曲，以便这些缺陷可以优先考虑风险 BAV修复手术的分层和计划。这项工作将通过实现三个具体目标来进行： (1)设计并评估用于BAV 4D重建的自动分割和建模算法 从多个临床成像模态的设备;（2）表征形态和动态特征 的BAV能力，并创建一个机器学习方法，用于全面的异常检测， 顺应性BAV;（3）使用从瓣膜修复中采集的图像评价BAV修复计划系统 在三个机构中。拟议的项目利用了两个方面的互补优势 模态：实时3D经食管超声心动图和4D计算机断层扫描血管造影， 其捕获具有高空间分辨率的主动脉尖的形态细节和 具有高时间分辨率的3D BAV装置。该项目的创新之处在于， 可能会改变BAV维修计划的执行方式。而不是依靠术中检查 当瓣膜未加压时，外科医生可以交互式地可视化图像衍生的BAV模型并量化 当瓣膜处于术前4D生理状态时，AR的动态机制。的意义 这项研究是，它可以促进一致性，在瓣膜修复计划跨机构，减少 外科医生对直觉和试错法的依赖，从而增加了BAV修复术在年轻人中的应用 成年人了这将具有相对于常规瓣膜置换的生活质量优势，常规瓣膜置换需要 终身抗凝治疗（机械瓣膜）或由于耐久性有限而多次再次置换 （生物瓣膜）。最后，对计算机辅助瓣膜的多模态图像数据进行了系统的分析 缺陷检测将广泛有益于后天性和先天性心脏病外科治疗的进步 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.