International Journal on Magnetic Particle Imaging IJMPI
Vol 9 No 1 Suppl 1 (2023): Int J Mag Part Imag
Self-supervised signal denoising in magnetic particle imaging
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Various noises restrict magnetic particle imaging (MPI) to achieve higher resolution and sensitivity in practice. In this study,we proposed a self-supervised learning method to denoise MPI signals. The deep learning-based architecture consisted with fourencoder’s blocks (EcBs) and four decoder’s blocks (DcBs). This model was trained with limited data of MPI magnetization signals to efficiently suppress noise related features by directly learning from the noisy signals. Simulated experiments showed that the selfsupervised method could reduce the noise interference in MPI signals and eventually improve image quality