DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging

doi:10.1088/1361-6560/ad13cf

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging
	Peng, Zhengyao 1,2,3; Yin, Lin 1,2,3; Sun, Zewen 1,2,3; Liang, Qian 1,2,3; Ma, Xiaopeng 4; An, Yu 1,3,5,6; Tian, Jie 1,3,5,6; Du, Yang 1,2,3
刊名	PHYSICS IN MEDICINE AND BIOLOGY
	2024-01-07
卷号	69 期号:1 页码:15
关键词	magnetic particle imaging end-to-end reconstruction deep learning image reconstruction
ISSN号	0031-9155
DOI	10.1088/1361-6560/ad13cf
通讯作者	An, Yu(yuan1989@buaa.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Du, Yang(yang.du@ia.ac.cn)
英文摘要	Objective. Magnetic particle imaging (MPI) shows potential for contributing to biomedical research and clinical practice. However, MPI images are effectively affected by noise in the signal as its reconstruction is an ill-posed inverse problem. Thus, effective reconstruction method is required to reduce the impact of the noise while mapping signals to MPI images. Traditional methods rely on the hand-crafted data-consistency (DC) term and regularization term based on spatial priors to achieve noise-reducing and reconstruction. While these methods alleviate the ill-posedness and reduce noise effects, they may be difficult to fully capture spatial features. Approach. In this study, we propose a deep neural network for end-to-end reconstruction (DERnet) in MPI that emulates the DC term and regularization term using the feature mapping subnetwork and post-processing subnetwork, respectively, but in a data-driven manner. By doing so, DERnet can better capture signal and spatial features without relying on hand-crafted priors and strategies, thereby effectively reducing noise interference and achieving superior reconstruction quality. Main results. Our data-driven method outperforms the state-of-the-art algorithms with an improvement of 0.9-8.8 dB in terms of peak signal-to-noise ratio under various noise levels. The result demonstrates the advantages of our approach in suppressing noise interference. Furthermore, DERnet can be employed for measured data reconstruction with improved fidelity and reduced noise. In conclusion, our proposed method offers performance benefits in reducing noise interference and enhancing reconstruction quality by effectively capturing signal and spatial features. Significance. DERnet is a promising candidate method to improve MPI reconstruction performance and facilitate its more in-depth biomedical application.
资助项目	Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[62027901] ; Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[82272111] ; Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[92159303] ; Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[81871514] ; Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[61901472,62201570] ; Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089[82230067] ; National Natural Science Foundation of China[7212207] ; National Natural Science Foundation of China[4332058] ; Beijing Natural Science Foundation
WOS关键词	TRACER ; NANOPARTICLE
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:001128968900001
资助机构	Beijing Municipal Natural Science Foundation https://doi.org/10.13039/501100005089 ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54991]
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	An, Yu; Tian, Jie; Du, Yang
作者单位	1.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Beijing Key Lab Mol Imaging, Beijing, Peoples R China 4.Shandong Univ, Sch Control Sci & Engn, Jinan, Shandon, Peoples R China 5.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China 6.Beihang Univ, Sch Engn Med, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Peng, Zhengyao,Yin, Lin,Sun, Zewen,et al. DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging[J]. PHYSICS IN MEDICINE AND BIOLOGY,2024,69(1):15.
APA	Peng, Zhengyao.,Yin, Lin.,Sun, Zewen.,Liang, Qian.,Ma, Xiaopeng.,...&Du, Yang.(2024).DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging.PHYSICS IN MEDICINE AND BIOLOGY,69(1),15.
MLA	Peng, Zhengyao,et al."DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging".PHYSICS IN MEDICINE AND BIOLOGY 69.1(2024):15.