Deep learning-based noise reduction method for the system matrix in magnetic particle imaging

Objective. Magnetic particle imaging (MPI) is an emerging imaging technique based on superparamagnetic iron oxide nanoparticles, offering high sensitivity and rapid imaging. However, in measurement-based MPI, image quality is degraded by noise arising during both the system matrix (SM) calibration procedure and the signal acquisition process. This study aims to develop a deep learning-based model for efficient noise suppression to enhance MPI image quality.Approach. We propose a hybrid encoder-decoder network integrating residual blocks (Res-Blocks) and swin transformer modules. The model employs a multi-scale feature extraction strategy to disentangle noise from valid signals, coupled with cross-level feature fusion to optimize frequency-domain recovery.Main results. Model performance was evaluated on simulated dataset, OpenMPI dataset, and dataset acquired from in-house MPI systems. The denoised SM achieved an average 12 dB improvement in signal-to-noise ratio (SNR). Reconstructed images showed better visual quality, with a peak SNR of 29.11 dB and a structural similarity index of 0.93, which outperformed the compared approaches.Significance. This work provides a robust solution for noise suppression in SM to enhance MPI image quality. The noise suppression framework is extensible to other SM-based medical imaging modalities.