  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).
