During percutaneous coronary intervention, conventional 2D X-ray imaging lacks depth information, making it difficult for clinicians to determine the 3D position of the guidewire. While some recent approaches incorporate micro-sensors to assist with pose estimation, many rely on implanted electromagnetic sensors, which can introduce additional clinical risks. In the paper, we present a non-invasive alternative by using an external 3-axis electronic magnetometer array. We further propose a Local-Global Magneto-Visual Network 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.
Wei et al. (Wed,) studied this question.