Deep dynamic coronary roadmap guidance in percutaneous coronary intervention: a deep learning approach

A dynamic coronary roadmap (DCR) serves as an intra-procedural navigation aid in percutaneous coronary interventions. However, the current technology relies on single-frame coronary artery segmentation through filtering, which struggles to adjust for positional shifts from heartbeat and respiratory movements, resulting in incomplete roadmap alignment of the coronary arteries and guidewire segments. To address these challenges, we propose a deep DCR method that integrates three models: deep-learning-based segmentation of coronary arteries and catheters, catheter tip detection, and optical-flow-based heart rate prediction. By leveraging optical flow, the system extracts heartbeat-related information directly from coronary artery angiography (CAG) images, without requiring ECG input during operation in sequences that meet the applicability conditions. Deep DCR uses a semi-supervised-learning-based segmentation model to generate a vascular roadmap, which is overlaid on the coronary artery for catheter segmentation. This overlay aligns the roadmap by capturing the relationship between the catheter and coronary artery motion during the transition from contrast medium injection to dissipation. This approach significantly enhances alignment accuracy, as demonstrated through case evaluations and guidewire coverage experiments. Our method provides a more precise and reliable DCR for intra-procedural navigation.