中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
self-supervised Signal Denoising in Magnetic Particle Imaging

文献类型:会议论文

作者Peng, Huiling1,2,4; Tian, Jie1,3,4,5; Hui, Hui1,2,4
出版日期2023-03-19
会议日期2023-3-22
会议地点Aachen, Germany
英文摘要

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 four encoder’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 self- supervised method could reduce the noise interference in MPI signals and eventually improve image quality.

源URL[http://ir.ia.ac.cn/handle/173211/52100]  
专题自动化研究所_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, Beijing, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
3.Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People’s Republic of China, Beijing, China
4.Beijing Key Laboratory of Molecular Imaging, Beijing, China
5.Zhuhai Precision Medical Center, Zhuhai People’s Hospital, affiliated with Jinan University, Zhuhai, China
推荐引用方式
GB/T 7714
Peng, Huiling,Tian, Jie,Hui, Hui. self-supervised Signal Denoising in Magnetic Particle Imaging[C]. 见:. Aachen, Germany. 2023-3-22.

入库方式: OAI收割

来源:自动化研究所

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