Arrhythmia-resolved 5D Flow MRI in Atrial Fibrillation and Stroke

项目摘要 心房颤动是最常见的持续性心律失常，是缺血性心脏病的主要危险因素， 中风这是由于左心房血栓形成，可通过抗凝治疗缓解 但也有出血的风险平衡出血风险的当前临床治疗选择策略 抗凝治疗对卒中风险缺乏 为 中风 患者 的 被 预测 预测能力。血瘀是魏尔啸三联征的关键组成部分 血栓形成和血流成像方法显示左心房速度降低与 风险然而，这些方法要么缺乏全面的3D测量，要么在测量时必须拒绝数据。 在心律失常中，这两种方法对于开发准确的生物标志物来预测中风都是必要的。 本项目的目标是进一步开发和优化心律失常分辨的流动成像MRI方法， 能够在可能发生卒中的心律失常期间捕获随时间变化的3D心房血流动力学 生物标志物来指导药物治疗选择。 C 该提案的第一个目的是开发和优化MRI方法：心律失常分辨5D流 MRI，多维数据集的维度：3D空间+心脏时间+心跳持续时间。了几 心律失常期间测量的心跳持续时间（例如，短心跳，长心跳），我们将构建3D 对于该心跳持续时间的一个心动周期的时变幅度和速度数据集。脉动流 能够模拟已知心律失常的体模将允许优化采集时间和图像 重建方法和参数。我将开发两种不同的重建算法， 使用相同的原始数据重建。首先，将重建图像以测量最大差异 以分析患者体内的心率可变血液动力学。 第二，将根据从固定的标准化心跳长度获取的数据重建图像， 允许在受试者之间进行比较，控制心跳持续时间本身的影响。这将受到考验 在此设置中，与参考标准相比：2D真实的时间相位对比。临床合作者将帮助 优化的成像重建的算法设计，以确保成功的体外到体内的翻译， 从第一个目标到第二个目标。第二个目标将在体内验证这项技术与队列 10例健康对照组和10例持续性房颤患者。这也将建立心率变量， 控制这些组之间的血流动力学差异。在第三个目标中，我们将测试 方法区分卒中史和无卒中史房颤患者。12例持续性心房 有既往心源性卒中史的房颤患者和12例无心源性卒中史的持续性房颤患者。 将招募中风史。目的是观察组间心房血流动力学的差异 作为开发心房血液动力学生物标志物以改善医学治疗选择的先驱。

Project Summary Atrial Fibrillation is the most common sustained cardiac arrhythmia, and it is a major risk factor for ischemic stroke. This is due to thrombus formation in the left atrium which may be mitigated by anticoagulation therapy but carrying its own bleeding risk. Current clinical therapy selection strategies balancing bleeding risk of anticoagulation therapy to stroke risk lack for stroke patient The be prediction predictive power. Blood stasis is a key component of Virchow's triad thrombus formation, and flow imaging methods have shown lowered left atrial velocities are associated with risk. However, these methods either lack omprehensive 3D measurement or must reject data when the is i n arrhythmia, where both of these may be necessary to develop accurate biomarkers to predict stroke. goal of this project is to further develop and optimize an arrhythmia resolved flow imaging MRI method to able to capture the time varying 3D atrial hemodynamics during arrhythmia with the potential to develop stroke biomarkers to guide medical therapy selection. c The first aim of this proposal is development and optimization of the MRI method: arrhythmia resolved 5D flow MRI, a multi-dimensional data set of dimension: 3D spatial + cardiac time + heartbeat duration. For several heartbeat durations measured during arrhythmia (e.g. short heartbeats, long heartbeats) we will construct a 3D time varying magnitude and velocity data set for one cardiac cycle for that heartbeat duration. A pulsatile flow phantom, capable of simulating a known arrhythmia, will allow the optimization of acquisition time and image reconstruction methodology and parameters. I will develop two different reconstruction algorithms, which can be reconstructed using the same raw data. First, images will be reconstructed to measure maximum differences across the varying heart rhythm in order to analyze the heart rate variable hemodynamics within a patient. Second, images will be reconstructed based on data acquired from fixed, standardized heartbeat lengths, allowing the comparison between subjects, controlling for the effect of heartbeat duration itself. This will be tested against the reference standard, in this setting: 2D real time phase contrast. Clinical collaborators will help with algorithm design of the optimized imaging reconstruction to ensure success of in vitro to in vivo translation of the technique from the first aim to subsequent aims. The second aim will validate this technique in vivo with a cohort of 10 healthy controls and 10 persistent atrial fibrillation patients. This will also establish heart rate variable and controlled hemodynamic differences between these groups. In the third aim, we will test the abilities of the method to discriminate between stroke history and no stroke history atrial fibrillation patients. 12 persistent atrial fibrillation patients with prior cardioembolic stroke history and 12 persistent atrial fibrillation patients without a stroke history will be recruited. The goal of this aim is to see differences in atrial hemodynamics between groups as a precursor to developing an atrial hemodynamic biomarker to improve medical therapy selection.