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BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging

文献类型：期刊论文

作者Wei, Zechen1,2; Liu, Yanjun3,4,5; Zhu, Tao1,2; Yang, Xin1,2; Tian, Jie4,5,6; Hui, Hui1,2
刊名IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
出版日期2023-12-12
页码15
ISSN号2471-285X
关键词Magnetic particle imaging deep learning self-attention mechanism time-frequency spectrum background signal
DOI10.1109/TETCI.2023.3337342
通讯作者Yang, Xin(xin.yang@ia.ac.cn) ; Hui, Hui(hui.hui@ia.ac.cn)
英文摘要Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality, which uses the nonlinear response of superparamagnetic iron oxide nanoparticles to the applied magnetic field to image their spatial distribution. Background signal is the main source of artifacts in MPI, which mainly includes harmonic interference and Gaussian noise. For different sources of noise, the existing methods directly process the time domain signal to achieve signal enhancement or construct system function by frequency domain signal to obtain high-quality reconstructed images. However, due to the randomness and variety of the background signal, the existing methods fail to eliminate all kinds of noise at the same time, especially when the noise is nonlinear. In this work, we proposed a deep learning method adopting self-attention mechanism, which can effectively suppress different levels of harmonic interference and Gaussian noise simultaneously. Our method deals with the two-dimensional time-frequency spectrum acquired by short-time Fourier transform from the temporal signal, learning global features and local features between time and frequency domain through the network, to achieve the purpose of reducing background noise. The performance of our method is analyzed via simulation experiments and imaging experiments performed with an in-house MPI scanner, which shows that our method can effectively suppress background signals and obtain high-quality MPI images.
WOS关键词RECONSTRUCTION ; SENSITIVITY ; RESOLUTION ; TRACER
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001134429900001
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/54868]  
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Yang, Xin; Hui, Hui
作者单位1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing 100190, Peoples R China
4.Beihang Univ, Sch Engn Med, Beijing 100190, Peoples R China
5.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100190, Peoples R China
6.Beihang Univ, Inst Automat, Minist Ind & Informat Technol Peoples Republ China, Key Lab Big Data Based Precis Med,CAS Key Lab Mol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Zechen,Liu, Yanjun,Zhu, Tao,et al. BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2023:15.
APA Wei, Zechen,Liu, Yanjun,Zhu, Tao,Yang, Xin,Tian, Jie,&Hui, Hui.(2023).BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,15.
MLA Wei, Zechen,et al."BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2023):15.

入库方式： OAI收割

来源：自动化研究所

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