Coronary Artery Disease (CAD) is one of the most common forms of heart disease, which is caused by a buildup of atherosclerotic plaque (known as stenosis) in the coronary arteries, leading to insufficient supplement of blood, oxygen, and nutrients to the heart. Fractional Flow Reserve (FFR), measuring the pressure ratio between the aorta and distal coronary artery, is an invasive physiologic gold standard for assessing the severity of coronary artery stenosis. Despite its benefits, invasive FFR assessment is still underutilized due to its high cost, time-consuming, experimental variability, and increased risk to patients. In this study, an attention-based multi-fidelity machine learning model (AttMulFid) is proposed for computationally efficient and accurate FFR assessment with uncertainty measurement. Within AttMulFid, an autoencoder is utilized to intelligently select geometric features from coronary arteries, with additional attention on the key area. Results show that the geometric features are able to represent the entirety of the geometric information and intelligently allocate attention based on crucial properties of geometry. Furthermore, the AttMulFid is a feasible approach for non-invasive, rapid, and accurate FFR assessment (with 0.002s/simulation).

翻译：冠状动脉疾病（CAD）是最常见的心脏病形式之一，由冠状动脉中动脉粥样硬化斑块（称为狭窄）积聚引起，导致心脏的血液、氧气和营养物质供应不足。血流储备分数（FFR）通过测量主动脉与冠状动脉远端的压力比，是评估冠状动脉狭窄严重程度的侵入性生理学金标准。尽管具有诸多优势，但侵入性FFR评估因其高成本、耗时、实验变异性大以及患者风险增加而仍未被充分利用。本研究提出了一种基于注意力的多保真机器学习模型（AttMulFid），用于实现计算高效、准确且带有不确定性测量的FFR评估。在AttMulFid中，利用自动编码器智能地从冠状动脉中提取几何特征，并对关键区域施加额外注意力。结果表明，几何特征能够代表几何信息的整体性，并根据几何的关键属性智能分配注意力。此外，AttMulFid是一种可行的方法，可用于无创、快速且准确的FFR评估（每模拟0.002秒）。