Predicting Stenosis in Coronary Arteries based on Deep Neural Network using Non-Contrast and Contrast Cardiac CT images

Abstract

In this paper, we demonstrate two different methods to predict stenosis, given non-contrast and contrast heart CT scan images, respectively. As far as we know, non-contrast heart CT images have been hardly used for predicting stenosis, since non-contrast CT images generally do not show the coronary arteries (LCX, LAD, RCA, LMT) distinctively. However, if it is possible to predict stenosis with non-contrast CT images, we believe it is beneficial for patients because they do not suffer from side effects of contrast agents. Our demonstration for non-contrast CT image depends upon the relationship between calcification and stenosis. According to physicians, 90% of stenosis accompanies calcification in coronary arteries. On the other hand, we have also conducted experiments with contrast heart CT scan images, where coronary arteries are rendered as “straightened circumferentially”. This second approach using contrast CT image can be reduced to binary classification problem. From our experiments, we demonstrate that our two approaches defined as multi-label, multi-class classification problem using non-contrast CT images and binary classification problem using contrast CT images, respectively, with deep neural networks as classifiers, are very promising. We also note that our data in non-contrast and contrast CT images have both able-bodied (or healthy) subjects as well as patients, which makes us believe it is practical when the methods are incorporated into supporting a real stenosis diagnosis system.