Double-Branched and Multi-Magnetic Directions Feature Fusion Network (DB&MDF2-Net) for the Accurate Reconstruction of Magnetic Particle Imaging

Objective: Magnetic particle imaging (MPI) is a novel non-destructive medical imaging method that visualizes the spatial distribution of superparamagnetic iron oxide nanoparticles. However, due to the non-uniformity of the selection and drive field, the unsatisfactory of the receive coil and the different components of the magnetization signal (induced electromotive force) detected by the orthogonal coil, processing the voltage signals measured by the receiving coils in different directions without discrimination will affect the reconstruction quality. Methods: This study introduces the Double-Branched and Multi-Magnetic Directions Feature Fusion Network (DB&MDF2-Net) to address these challenges. The dual-branch(DB) strategy processes X and Y-directional magnetic field components independently, reducing information confusion. Each branch has a dual-sampling feature(DSF) layer that captures multi-scale spatial information and preserves spatial structure, enhancing the extraction of particle distribution and edge details. Additionally, a multi-head self-attention transformer(MSA-T) layer efficiently integrates features from different modules, allowing the network to learn complex inter-feature relationships. Results: The effectiveness of the DB strategy, DSF and MSA-T layers in our proposed method were validated through ablation experiments. Simulate and phantom experiments further demonstrate significant improvements in detail capture and anti-noise capability of DB&MDF2-Net without any hardware modifications, enabling more precise restoration of real particle distribution characteristics. Conclusion: These findings suggest that DB&MDF2-Net can significantly improve the imaging accuracy of MPI. Significance: This research is expected to enhance the practicality of MPI in biomedical applications and contribute to the future development of MPI technology.