Coronary Artery Disease (CAD) is one of the most common forms of heart disease, caused by a buildup of atherosclerotic plaque in the coronary arteries. When this buildup is extensive, it can result in obstructions in the lumen of the blood vessels (known as stenosis) that lead to insufficient delivery of essential molecules like oxygen to the heart. Fractional Flow Reserve (FFR), defined as the ratio of pressures distal and proximal to the stenosis, is the physiologic gold standard for assessing the severity of CAD in the cardiac catheterization laboratory and relies upon the placement of an invasive coronary wire. Despite its strong diagnostic value, invasive FFR assessment is underutilized due to its cost, time-consuming nature, technique-dependent variability, and the small potential of increased risk to the patient. In this study, an attention-based multi-fidelity machine learning model (AttMulFid) is proposed for efficient and accurate virtual FFR (vFFR) assessment, including uncertainty quantification, without the use of an invasive coronary wire. Within AttMulFid, an autoencoder is used to select geometric features from the coronary arteries, with additional attention to the stenosis region. A convolutional neural network (feature fusion U-Net) combines multi-fidelity data, geometric features, and boundary conditions to produce accurate estimates of vFFR. We present results that demonstrate the good performance of AttMulFid against CFD FFR data, as well as in vivo, invasive FFR assessment from patients. Our results also show that the selected geometric features learned by the autoencoder can accurately represent the entire geometry, with greater attention on key features such as stenosis. AttMulFid thus presents itself as a feasible approach for non-invasive, rapid, and accurate vFFR assessment.

中文翻译：

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基于注意力的多保真机器学习模型，用于分数流量储备评估


冠状动脉疾病 （CAD） 是最常见的心脏病之一，由冠状动脉中动脉粥样硬化斑块的积聚引起。当这种积聚广泛时，会导致血管腔阻塞（称为狭窄），从而导致氧气等必需分子无法输送到心脏。血流储备分数 （FFR） 定义为狭窄远端和近端压力的比值，是心导管室评估 CAD 严重程度的生理金标准，并依赖于侵入性冠状动脉导丝的放置。尽管具有很强的诊断价值，但由于其成本、耗时、技术依赖性可变性以及增加患者风险的可能性很小，侵入性 FFR 评估未得到充分利用。在这项研究中，提出了一种基于注意力的多保真机器学习模型 （AttMulFid），用于高效和准确的虚拟 FFR （vFFR） 评估，包括不确定性量化，而无需使用侵入性冠状动脉线。在 AttMulFid 中，自动编码器用于从冠状动脉中选择几何特征，并额外关注狭窄区域。卷积神经网络（特征融合 U-Net）结合了多保真度数据、几何特征和边界条件，以生成 vFFR 的准确估计值。我们提出的结果证明了 AttMulFid 对 CFD FFR 数据以及患者体内侵入性 FFR 评估的良好性能。我们的结果还表明，自动编码器学习到的选定几何特征可以准确地表示整个几何结构，并更加关注狭窄等关键特征。 因此，AttMulFid 将自己呈现为一种非侵入性、快速和准确的 vFFR 评估的可行方法。