  Functionally significant coronary artery disease (CAD) is caused by plaque
buildup in the coronary arteries, potentially leading to narrowing of the
arterial lumen, i.e. coronary stenosis, that significantly obstructs blood flow
to the myocardium. The current reference for establishing the presence of a
functionally significant stenosis is invasive fractional flow reserve (FFR)
measurement. To avoid invasive measurements, non-invasive prediction of FFR
from coronary CT angiography (CCTA) has emerged. For this, machine learning
approaches, characterized by fast inference, are increasingly developed.
However, these methods predict a single FFR value per artery i.e. they don't
provide information about the stenosis location or treatment strategy. We
propose a deep learning-based method to predict the FFR along the artery from
CCTA scans. This study includes CCTA images of 110 patients who underwent
invasive FFR pullback measurement in 112 arteries. First, a multi planar
reconstruction (MPR) of the artery is fed to a variational autoencoder to
characterize the artery, i.e. through the lumen area and unsupervised artery
encodings. Thereafter, a convolutional neural network (CNN) predicts the FFR
along the artery. The CNN is supervised by multiple loss functions, notably a
loss function inspired by the Earth Mover's Distance (EMD) to predict the
correct location of FFR drops and a histogram-based loss to explicitly
supervise the slope of the FFR curve. To train and evaluate our model,
eight-fold cross-validation was performed. The resulting FFR curves show good
agreement with the reference allowing the distinction between diffuse and focal
CAD distributions in most cases. Quantitative evaluation yielded a mean
absolute difference in the area under the FFR pullback curve (AUPC) of 1.7. The
method may pave the way towards fast, accurate, automatic prediction of FFR
along the artery from CCTA.
