Image guidance during percutaneous coronary interventions (PCI) can help minimize radiation exposure and contrast use while ensuring procedural safety and efficacy. To support this, this work proposes a framework that leverages a patient-specific cardio-respiratory motion model, optimized intra-procedurally, to enable real-time vessel tracking. The approach is built on: (i) a population-derived motion model capturing cardiac and respiratory dynamics, and (ii) an automated coronary artery segmentation pipeline for both 3D computed tomography angiography (CTA) and 2D x-ray angiography (XA). The motion model integrates cardiac phase and respiratory surrogates, including cycle phase and inhalation/exhalation ratio. To enable training and validation, paired 3D+t CTA and 2D+t XA sequences are synthetically generated using the proposed motion model. Coronary artery segmentation is performed using a dual-convolution-transformer U-Net. The approach was evaluated by comparing the segmented left ventricle across simulated and ground-truth 4D cardiac Magnetic Resonance Angiography datasets, demonstrating volume consistency within the 95% confidence interval. Segmentation achieved high Dice similarity scores: 0.86 ± 0.02 (CTA), 0.98 ± 0.01 (simulated XA), and 0.78 ± 0.01 (real XA). These results validate the accuracy of the synthetic motion simulation and segmentation pipeline. Future steps involve tracking of vessels by estimating patient-specific cardio-respiratory motion by using the proposed population-derived motion and segmented coronary arteries.