Harmonic-Aware Transformer for Real-Time Catheter Localization in Interventional Procedures of Magnetic Particle Imaging

Objective. Magnetic particle imaging (MPI) enables real-time, radiation-free tracking of magnetic nanoparticle–coated instruments, making it highly suitable for interventional procedures. This study proposes a harmonic-aware transformer framework that directly predicts catheter tip positions from raw MPI voltage signals, eliminating the need for image reconstruction and reducing computational latency. Approach. The framework incorporates frequency-domain preprocessing to isolate the 2nd–8th drive-field harmonics, enhancing the signal-to-noise ratio while preserving motion-relevant features. A transformer architecture with six encoder layers and eight attention heads is employed to learn spatio-temporal dependencies across the three receive axes ( x, y, z) for accurate three dimensional position estimation. The model is trained on simulated MPI signals and evaluated on real in vitro datasets under standard, bending, and heartbeat-like motion conditions. Main results. The proposed method achieves sub-millimeter localization accuracy, with a minimum L2 error of 0.103±0.092 mm and mean absolute errors (MAEs) of 0.039±0.046 mm, 0.054±0.049 mm, and 0.060±0.044 mm along the (x,y,z) axes, respectively, for the bending dataset. Across all datasets, the MAE ranges from 0.165 mm to 0.655 mm, demonstrating consistent performance. The optimized inference achieves a latency of 0.55 ms per frame and a throughput of approximately 1800 frames s −1, confirming real-time capability. Significance. Compared with conventional MPI-guided approaches relying on image reconstruction, the proposed framework provides improved accuracy, reduced latency, and enhanced robustness under complex motion conditions. These results highlight the potential of harmonic-aware transformer models as efficient and scalable solutions for real-time catheter localization in interventional MPI.