Multi-scale Dual Domain Network for Nonlinear Magnetization Signal Filtering in Magnetic Particle Imaging

CASIA OpenIR

	Multi-scale Dual Domain Network for Nonlinear Magnetization Signal Filtering in Magnetic Particle Imaging
	IEEE Engineering in Medicine and Biology Society
	2023-08
简介	Magnetic particle imaging (MPI) is a medical imaging technology with high resolution and high sensitivity, which tracks the distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in the nonlinear response to dynamic excitation at a field-free region. However, various noises distort the signals resulting in a decline in imaging quality. Traditional threshold-based methods cannot remove dynamic noise in MPI signals. Therefore, a self-supervised denoising method is proposed to denoise MPI signals in this study. The approach adopted U-net as the backbone and modified the network for MPI signals. The network is trained using two periods of noisy signals and the shape prior knowledge of the MPI signals is introduced for promoting the convergence of the self-supervised net. The experiments show that the learning-based method can still denoising the MPI signal without labeling data and eventually improve image quality, and our approach can achieve the best performance compared with other self-supervised methods in MPI signal denoising.
学科门类	工学::计算机科学与技术（可授工学、理学学位）
收录类别	SCI
语种	英语
文献类型	期刊
条目标识符	http://ir.ia.ac.cn/handle/173211/52025
专题	中国科学院自动化研究所
推荐引用方式 GB/T 7714	IEEE Engineering in Medicine and Biology Society.Multi-scale Dual Domain Network for Nonlinear Magnetization Signal Filtering in Magnetic Particle Imaging,2023.

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