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.

翻译：功能性显著的冠状动脉疾病（CAD）由冠状动脉中的斑块积聚引起，可能导致动脉管腔狭窄（即冠状动脉狭窄），严重阻碍心肌供血。目前确定是否存在功能性显著狭窄的参考标准是有创性血流储备分数（FFR）测量。为避免有创测量，基于冠状动脉CT血管造影（CCTA）的无创FFR预测方法应运而生。为此，以快速推理为特征的机器学习方法得到日益发展。然而，这些方法仅能预测每条动脉的单个FFR值，无法提供关于狭窄位置或治疗策略的信息。我们提出一种基于深度学习的方法，从CCTA扫描中预测沿动脉的FFR分布。本研究纳入110例患者的CCTA图像，这些患者在112条动脉中接受了有创性FFR回撤测量。首先，将动脉的多平面重建图像输入变分自编码器，通过管腔面积和无监督动脉编码表征动脉特征。随后，卷积神经网络预测沿动脉的FFR分布。该CNN由多个损失函数监督，特别是受推土机距离启发的损失函数，用于准确预测FFR下降的位置，以及基于直方图的损失函数，用于显式监督FFR曲线的斜率。为训练和评估模型，我们执行了八折交叉验证。所得FFR曲线与参考值具有良好一致性，在多数情况下能区分弥漫性和局灶性CAD分布。定量评估显示，FFR回撤曲线下面积的平均绝对差值为1.7。该方法有望为从CCTA快速、准确、自动预测沿动脉的FFR分布奠定基础。