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

心房颤动（AF）是最常见的心律失常，影响全球约3500万人。 在房颤期间，心脏的两个上腔（心房）微弱且不规则地搏动，产生缓慢血流区域 （血液淤滞）可能形成凝块的地方。凝块优先在左心耳（LAA）内形成， 进入大脑导致中风抗凝治疗可降低房颤患者栓塞性卒中的风险 但是，由于相关的出血风险增加，这些药物不推荐用于所有 房颤患者。确定抗凝治疗是否有益需要评估患者的卒中风险是否超过 出血风险。然而，目前对房颤患者进行卒中风险分层的方法并不个性化， 大量的患者，不确定抗凝是否有益。的主要目标 本项目旨在开发新的CT成像分析，以量化左心耳血栓形成的个体化风险 AF患者中。我们的科学前提是，血瘀是血栓形成的关键因素，因为它允许 血栓形成活性物质相互作用并引发凝块形成。我们的初步数据显示- 心房和左心耳中血流和室壁运动的时间动力学与血栓密切相关 阵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 通过心房动力学的4D CT成像结合计算 流体动力学（CFD）。我们将开发图像处理算法，以量化基于时间的左心房动力学- 分辨CT扫描，包括造影剂混浊的时空动态、成像的室壁运动，以及 LAA中血流的非牛顿流变学。在目标2中，我们将建立4D之间的关系 心房动力学采用多心跳对比CT，血瘀采用CFD，以便于临床 仅通过CT进行静态映射的平移。我们还将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预测 左心耳淤血在目标3中，我们将进行一项基于结果的临床试点研究，以制定个性化的 基于图像的血栓形成风险评分。我们将采集有左心耳血栓或房颤病史的患者的CT数据， 相关中风和无血栓形成史的AF患者的匹配对照组。我们将使用 这一独特的数据集用于开发基于患者特定图像的风险评分， 分析功能和几何参数。我们的团队包括一位具有物理学背景的心脏病专家 一位是专门从事成像的工程师，一位是专门从事CFD分析的工程师，一位是专门从事定量分析的工程师。 心脏成像分析。我们的翻译目标是为临床医生提供一种新的基于图像的工具 用于房颤患者的个性化风险分层，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.