Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis

Treatment of patients with obstructive coronary artery disease is guided by the functional significance of a coronary artery stenosis. Fractional flow reserve (FFR), measured during invasive coronary angiography (ICA), is considered the references standard to define the functional significance of a coronary stenosis. Here, we present an automatic method for non-invasive detection of patients with functionally significant coronary artery stenosis based on 126 retrospectively collected cardiac CT angiography (CCTA) scans with corresponding FFR measurement. We combine our previous works for the analysis of the complete coronary artery tree and the LV myocardium by applying convolutional autoencoders (CAEs) to characterize both, coronary arteries and the LV myocardium. To handle the varying number of coronary arteries in a patient, an attention-based neural network is trained to obtain a combined representation per patient, and to classify each patient according to the presence of functionally significant stenosis. Cross-validation experiments resulted in an average area under the receiver operating characteristic curve of 0.74, and showed that the proposed combined analysis outperformed the analysis of the coronary arteries or the LV myocardium alone. This may lead to a reduction in the number of unnecessary ICA procedures in patients with suspected obstructive CAD.