Deep-Learning-Based Registration of Diagnostic Angiogram and Live Fluoroscopy for Percutaneous Coronary Intervention

Abstract

Percutaneous coronary intervention (PCI) is an effective treatment for increasing blood flow in narrowed coronary arteries by implanting stents. When inserting a guidewire during PCI, a cardiologist watches two X-ray videos, a cine loop of angiography and a real-time video of live fluoroscopy to navigate the guidewire to the target location while avoiding entering wrong branches of blood vessels. However, watching the two separate X-ray videos increases a cardiologist’s mental efforts and intervention time. Moreover, more contrast agents may need to be injected to validate the correct insertion. To address this problem, dynamic coronary roadmapping (DCR) has been suggested because it provides registered images of two different X-ray images. In this study, we propose a novel patient-specific deep-learning-based approach for DCR that uses only two types of X-ray images without using an electrocardiogram (ECG). In the proposed approach, a trained deep generative model generates a registered image from the guidewire images in live fluoroscopy and angiographic sequence from diagnostic coronary angiography. Because the proposed approach can implicitly compensate for both cardiac and respiratory motions, it can be applied to arrhythmia patients with irregular ECG signals, unlike conventional image registration through ECG. Qualitative evaluation of 34 PCI cases, including three irregular heartbeat cases, showed low registration errors (26% lower than that of the latest work) and high image quality (more than 30 dB peak signal-to-noise ratio (PSNR)). All registration results were classified as “fit for use” (70.6% “Good” and 29.4% “Acceptable”) by three cardiologists. Furthermore, three irregular beating cases were classified as having “Good” registration quality.

Keywords

• Dynamic coronary roadmapping
• deep learning
• image registration
• X-ray angiography
• X-ray fluoroscopy