Cross-modal fusion of external magnetic sensing and simulated 2D imaging for 3D guidewire pose estimation

work framework (LG-MagNet) that fuses magnetic field information with image data to enable precise 3D pose estimation of the guidewire. Specifically, we first perform a shared encoder for cross-modal feature fusion. Then we employ convolutional operations that integrate local and global features. Finally, we utilize a lightweight prediction head for end-to-end depth regression. We constructed experimental equipment and collected a clinical simulation datasets. Results show a root mean square error (RMSE) of (0.797 ± 0.095 mm) for depth prediction along the Z-axis and an overall RMSE of (1.216 ± 0.072) mm for 3D guidewire shape reconstruction. Quantitative analysis indicates that fusing external magnetometer data with 2D imaging improves pose estimation stability, particularly in regions with curvature.