Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
self-supervised Signal Denoising in Magnetic Particle Imaging | |
Peng, Huiling1,2,3; Tian, Jie1,2,4,5; Hui, Hui1,2,3 | |
2023-03-19 | |
会议名称 | International Journal on Magnetic Particle Imaging |
会议日期 | 2023-3-22 |
会议地点 | Aachen, Germany |
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. |
收录类别 | 其他 |
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 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52100 |
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, Beijing, China 2.Beijing Key Laboratory of Molecular Imaging, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 4.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 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],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
self-supervised sign(870KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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