Endovascular interventions are procedures designed to diagnose and treat vascular diseases, using catheters to navigate inside arteries and veins. Thanks to their minimal invasiveness, they offer many benefits, such as reduced pain and hospital stays, but also present many challenges for clinicians, as they require specialized training and heavy use of X-rays. This is particularly relevant when accessing (i.e., cannulating) small arteries with steep angles, such as most aortic branches. To address this difficulty, we propose a novel solution that enhances fluoroscopic 2D images in real-time by displaying virtual configurations of the catheter and guidewire. In contrast to existing works, proposing either simulators or simple augmented reality frameworks, our approach involves a predictive simulation showing the resulting shape of the catheter after guidewire withdrawal without requiring the clinician to perform this task. Our system demonstrated accurate prediction with a mean 3D error of 2.4±1.3 mm and a mean error of 1.1±0.7 mm on the fluoroscopic image plane between the real catheter shape after guidewire withdrawal and the predicted shape. A user study reported an average intervention time reduction of 56 % when adopting our system, resulting in a lower X-ray exposure.