Right Ventricular Remodeling in Tetralogy of Fallot

项目摘要/摘要 法洛四联症(ToF)是最常见的青紫型先天性心脏病，患病率为0.3%。 在纠正之前，它的四个缺陷导致右心压升高和充氧和混合 脱氧血液。即使在手术修复后，患者可能会经历右心压升高和 残余肺狭窄、肺返流、疤痕形成和传导引起的体积 异常现象。几何形状、壁厚和压力-体积关系的这些变化都有助于 右室(RV)重构，最终可能导致室性心律失常等不良事件， 右室功能障碍和需要进行肺动脉瓣修补术，总体影响高达44%的患者。尽管有很大的 TOF患者的内科和外科治疗已取得进展，但了解仍然有限 其中，患者将经历不利的RV重构和随后的临床事件。TOF患者的心脏 功能通常每年使用心血管磁共振(CMR)成像进行评估，该成像提供 右心及其瓣膜的绝佳视野。然而，手动分析这些图像非常耗时 并且受制于用户间和用户内的可变性。此外，CMR还提供解剖和流数据支持 肺动脉血流动力学的量化，但尚未在修复后的ToF患者中进行研究。 通过计算的应用，详细描述了肺动脉的应力和压力 流体动力学(CFD)模拟可以提供对影响RV重塑的因素的洞察。那里仍然有一个 尚未满足的需求是，全面确定表征和预测主要ToF进展的特征 修复不良的RV重塑和不良结局。我在这项提案中的目标是确定结构 和与右室重构相关的血流动力学参数，以改善临床 关爱和生活质量。我计划用两个具体目标来实现这些目标。在目标1中，我将开发一个 使用CMR精确自动分割3D心脏体积的有监督机器学习算法 图像。这一算法将使心脏结构和功能的健壮和可重复测量成为可能 横截面和纵向分析。我假设这个算法将会达到精确和精确 分割结果通过Dice评分和组内相关系数来评估。在目标2中，我将学习 患者特定的肺动脉血流动力学，并确定与不利的右室重构的相关性。 具体地说，我将基于CMR派生的几何图形和相位对比度生成3D和1D CFD模型 流数据。我假设血流动力学参数，如壁面切应力和总阻力 将与房车重塑相关，并提供机械上的洞察。总体而言，我预计这个项目 将为我提供经验和技能，帮助我实现成为一名内科科学家的目标 心血管生理学、医学成像和流体动力学方面的专业知识，同时导致验证 将支持临床医生和研究人员了解ToF的技术。

Project Summary/Abstract Tetralogy of Fallot (ToF) is the most common cyanotic congenital heart disease, affecting 0.3% of children. Before correction, its four defects lead to increased right heart pressures and mixing of oxygenated and deoxygenated blood. Even after surgical repair, patients may experience elevated right heart pressures and volumes due to residual pulmonary stenosis, pulmonary regurgitation, scar formation, and conduction abnormalities. These changes in geometry, wall thickness, and pressure-volume relationships all contribute to right ventricular (RV) remodeling, which can eventually lead to adverse events such as ventricular arrhythmias, RV dysfunction, and the need for pulmonic valve repair, affecting up to 44% of patients overall. Despite the great advances that have been made in medical and surgical care of ToF patients, there is still limited understanding of which patients will experience adverse RV remodeling and subsequent clinical events. ToF patients’ cardiac function is normally assessed annually using cardiovascular magnetic resonance (CMR) imaging, which provides excellent views of the right heart and its valves. However, manual analysis of these images is time-consuming and subject to inter- and intra-user variability. Additionally, CMR provides anatomic and flow data enabling quantification of pulmonary artery hemodynamics, but has not yet been investigated in post-repair ToF patients. Detailed characterization of pulmonary artery stresses and pressures, through the application of computational fluid dynamics (CFD) simulations, could provide insight into factors affecting RV remodeling. There remains an unmet need to comprehensively identify features that characterize and predict progression from primary ToF repair to adverse RV remodeling and poor outcomes. My objectives in this proposal are to identify the structural and hemodynamic parameters of ToF that are associated with RV remodeling in order to improve both clinical care and quality of life. I plan to approach these objectives using two specific aims. In Aim 1, I will develop a supervised machine learning algorithm to accurately and automatically segment 3D cardiac volumes using CMR images. This algorithm will enable robust and repeatable measurements of cardiac structure and function for both cross-sectional and longitudinal analyses. I hypothesize that this algorithm will achieve accurate and precise segmentation results as assessed by Dice scores and intraclass correlation coefficients. In Aim 2, I will study patient-specific pulmonary artery hemodynamics and determine associations with adverse RV remodeling. Specifically, I will generate 3D and 1D CFD models based upon CMR-derived geometries and phase-contrast flow data. I hypothesize that hemodynamic parameters such as wall shear stress and total pathway resistance will be associated with and provide mechanistic insight into RV remodeling. Overall, I anticipate that this project will provide me with the experience and skills to help achieve my goal of becoming a physician-scientist with expertise in cardiovascular physiology, medical imaging, and fluid dynamics, while leading to validated technologies that will support clinicians and researchers in their understanding of ToF.