CASIA OpenIR  > 中国科学院分子影像重点实验室
Content-Noise Feature Fusion Neural Network for Image Denoising in Magnetic Particle Imaging
Tan Wang1,2,3; Liwen Zhang1; Zechen Wei1,2,3; Yusong Shen4; Jie Tian1,3,5; Hui Hui1,2,3
2023
会议名称IEEE Engineering in Medicine and Biology Conference
会议日期2023
会议地点Sydney Australia
出版者IEEE
摘要

Magnetic particle imaging (MPI) is a tomographic imaging method that quantitatively determines the distribution of magnetic nanoparticles (MNPs). However, the performance of MPI is primarily limited by the noise in the receive coil and electronic devices, which causes quantification errors for MPI images. Existing methods cannot efficiently eliminate noise while preserve structural details in MPI images. To address this problem, we propose a Content-Noise Feature Fusion Neural Network equipped with tailored modules of noise learning and content learning. It can simultaneously learn content and noise features of raw MPI images. Experimental results show that the proposed method outperforms the state-of-the-art methods on structural details preservation and image noise reduction of different levels.

收录类别EI
七大方向——子方向分类医学影像处理与分析
国重实验室规划方向分类AI For Science
是否有论文关联数据集需要存交
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/57480
专题中国科学院分子影像重点实验室
通讯作者Hui Hui
作者单位1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Beijing Key Laboratory of Molecular Imaging
4.School of Computer Science and Engineering, Southeast University
5.Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Tan Wang,Liwen Zhang,Zechen Wei,et al. Content-Noise Feature Fusion Neural Network for Image Denoising in Magnetic Particle Imaging[C]:IEEE,2023.
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