self-supervised Signal Denoising in Magnetic Particle Imaging | |
Peng, Huiling3,4,5; Tian, Jie1,2,4,5; Hui, Hui3,4,5 | |
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. |
内容类型 | 会议论文 |
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.Zhuhai Precision Medical Center, Zhuhai People’s Hospital, affiliated with Jinan University, Zhuhai, China 2.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 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 4.Beijing Key Laboratory of Molecular Imaging, Beijing, China 5.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 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. |
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