Patient-specific thrombosis risk in atrial fibrillation by 4D CT imaging of atrial kinetics combined with computational fluid dynamics

房颤(房颤)是最常见的心律失常，全世界约有3500万人受到影响。 在房颤期间，心脏的两个上腔(心房)搏动微弱且不规则，形成缓慢的血流区域 (血瘀症)可能形成血栓的地方。血栓优先形成于左心耳(LAA)，可 进入大脑会导致中风。抗凝治疗可降低房颤患者发生栓塞性中风的风险 药物治疗，但由于相关的出血风险增加，并不推荐对所有患者使用这些药物。 房颤患者。要确定抗凝是否有益，需要评估患者中风的风险是否大于 出血的风险。然而，目前对卒中房颤患者进行风险分层的方法并不是个性化的，对于 大量的患者，留下了抗凝是否有益的不确定性。的主要目标是 这个项目是开发新的CT成像分析来量化LAA血栓形成的个性化风险 在房颤患者中。我们的科学前提是，血瘀症是血栓形成的关键因素，因为它允许 导致血栓形成的活性物质相互作用并启动血栓形成。我们的初步数据显示空间- 心房和左心耳血流和室壁运动的时间动力学与血栓形成密切相关 队形。我们的方法包括三个具体目标。在目标1中，我们将开发和验证一个计算 心房动力学4DCT成像结合计算机辅助量化左房血瘀证的框架 流体动力学(CFD)。我们将开发基于时间的图像处理算法来量化左房动力学。 分辨率CT扫描，包括对比剂混浊的时空动态，成像的室壁运动，以及 LAA中血液流动的非牛顿流变学。在目标2中，我们将建立4D之间的关系 采用多心动对比CT心动图和CFD血瘀法进行心房动力学检查，以方便临床应用 单纯CT平移血瘀图。我们还将对不确定性进行第一次严格的分析 由成像分辨率、建模假设和生理变异性引起的结果传播到预测 LAA血瘀证。在目标3中，我们将进行一项基于结果的临床试点研究，以开发个性化的 基于图像的血栓形成风险评分。我们将获得有左心耳血栓或房颤病史的患者的CT数据。 与卒中相关的患者和无血栓病史的房颤患者的配对对照组。我们将使用 这一独特的数据集可开发包含CT对比度混浊的基于患者特定图像的风险评分 使用函数参数和几何参数进行分析。我们的团队包括一位有物理背景的心脏病专家 专门从事成像，一名具有CFD分析专业知识的工程师，以及一名具有定量专业知识的工程师 心脏影像分析。我们的翻译目标是为临床医生提供一种基于图像的新工具 用于对房颤患者进行个性化风险分层以指导抗凝决策 并改善结果。

Atrial fibrillation (AF) is the most common arrhythmia, affecting approximately 35 million people worldwide. During AF, the heart's two upper chambers (the atria) beat weakly and irregularly creating regions of slow flow (blood stasis) where clots may form. Clots preferentially form within the left atrial appendage (LAA) and can travel to the brain resulting in stroke. The risk of embolic strokes in AF patients is reduced with anticoagulation medications but, due to the associated increased bleeding risk, these medications are not recommended for all AF patients. Determining if anticoagulation is beneficial requires assessing if patients' risk of stroke outweighs the bleeding risk. However, current methods to risk-stratify AF patients for stroke are not personalized and, for a large number of patients, leave uncertainty as to whether anticoagulation is beneficial. The main objective of this project is to develop novel CT imaging analyses to quantify the personalized risk of LAA thrombosis in AF patients. Our scientific premise is that blood stasis is a key ingredient of thrombosis because it permits thrombogenic reactive species to interact and initiate clot formation. Our preliminary data suggest the spatio- temporal dynamics of blood flow and wall motion in the atrium and LAA strongly correlate with thrombus formation. Our approach consists of three specific aims. In Aim 1 we will develop and validate a computational framework to quantify left atrial blood stasis by 4D CT imaging of atrial kinetics combined with computational fluid dynamics (CFD). We will develop image processing algorithms to quantify left atrial kinetics based on time- resolved CT scans, including the spatio-temporal dynamics of contrast opacification, imaged wall motion, and the non-Newtonian rheology of blood flow in the LAA. In Aim 2 we will establish the relationship between 4D atrial kinetics by multi-heartbeat contrast CT and blood stasis using CFD, in order to facilitate the clinical translation of stasis mapping by CT alone. We will also perform the first rigorous analysis of how uncertainty caused by imaging resolution, modeling assumptions, and physiological variability propagates into predictions of LAA blood stasis. In Aim 3 we will perform an outcome-based clinical pilot study to develop a personalized image-based thrombosis risk score. We will acquire CT data in patients with a history of LAA thrombus or AF- associated stroke and a matched comparison group of AF patients with no history of thrombosis. We will use this unique data set to develop a patient-specific image-based risk score incorporating CT contrast opacification analyses with functional and geometric parameters. Our team includes a cardiologist with a physics background specializing in imaging, an engineer with expertise in CFD analysis, and an engineer with expertise in quantitative analyses of cardiac imaging. Our translational goal is to provide clinicians with a novel image-based tool for personalized risk stratification of patients with atrial fibrillation to guide anticoagulation decisions and improve outcomes.