Shangzhi Wu

This project brings coronary anatomy, interventional instruments and imaging feedback into one interactive research environment. It supports exploring guidewire and catheter manipulation, stent deployment and the effect of cardiac motion on the intervention scene. The focus is coronary intervention mechanics and digital twins for procedural research and robotic simulation training.

This project maps patient cardiac magnetic resonance (CMR) information into patient CT coordinates. CMR and CT describe complementary tissue and anatomical information, but different image geometry and incomplete coverage make direct comparison difficult. A template bridge and local refinement connect the two image spaces.

DSAVirtualLab combines CT-derived anatomy, controllable C-arm geometry and simulated X-rays. It provides alternative views for procedural review, teaching and interventional AI research. A biplane scene allows the relationship between projection angle, patient position and anatomical visibility to be explored.

Intravascular optical coherence tomography (IVOCT) records vessel cross-sections along a pullback. This project separates the main lumen, full lumen and bifurcation regions, connecting frame-level segmentation with longitudinal inspection and three-dimensional reconstruction. Keeping branch openings distinct from the main vessel wall is central to interpreting coronary side branches.

This project focuses on recognizing guidewires, catheters and stents in interventional X-ray images. Colored landmarks and skeletons make device shape and movement visible across frames. The research connects device recognition with pose estimation and tracking in image-assisted vascular interventions.