Deep learning-based noise reduction method for the system matrix in magnetic particle imaging.
Authors
Affiliations (5)
Affiliations (5)
- Southeast University, School of Computer Science and Engineering, Nanjing, Jiangsu, 210096, CHINA.
- Beihang University, School of Engineering Medicine, Beihang University, Beijing, People's Republic of China, Beijing, Beijing, 100091, CHINA.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, Beijing, 100091, CHINA.
- Southeast University, Southeast University, Department of Biology & Medical Eng. Laboratory of Image Science & Technology, Nanjing, Jiangsu, 210096, CHINA.
- CAS Key Laboratory of Molecular Imaging, CAS Institute of Automation, 95 Zhongguancun East Road, Beijing, CN, 100190, CHINA.
Abstract
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 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 system matrix achieved an average 12 dB improvement in signal-to-noise ratio (SNR). Reconstructed images showed better visual quality, with a peak signal-to-noise ratio (PSNR) of 29.11 dB and a structural similarity index (SSIM) of 0.93, which outperformed the compared approaches. 
Significance: This work provides a robust solution for noise suppression in system matrix to enhance MPI image quality. The noise suppression framework is extensible to other system matrix-based medical imaging modalities.