Automated right coronary artery localizer using deep learning for optimal cardiac phase selection

Abstract

Coronary computed tomography angiography (CCTA) is a major clinical imaging technique used to diagnose cardiovascular diseases. To improve diagnostic accuracy, it is necessary to determine the most optimal reconstruction phase that has the best image quality with little or no motion artifacts. The end-systolic phase and end-diastolic phase are the two commonly used phases for image reconstruction, but they are not always optimal in terms of motion-based image quality. In this paper we propose a deep learning method to automatically select an optimal phase based on a set of 2D axial phase image reconstructions. We select the right coronary artery (RCA) as our main vessel of interest to analyze reconstruction quality. Two deep convolutional neural networks are developed to perform efficient heart region segmentation and RCA localization without manually performing patient-specific image segmentation. We also demonstrate how to calculate image entropy as a figure of merit to evaluate RCA reconstruction quality. Results based on real clinical data with a heart rate of 74 beats per minute (bpm) have shown our proposed algorithm can efficiently localize the RCA for arbitrary cardiac phase, and accurately determine the optimal RCA reconstruction with minimal motion artifacts.