Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging

doi:10.1088/1361-6560/ace022

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

	Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging
	Zhang, Peng 4; Liu, Jie 4; Li, Yimeng 5,6,7; Zhu, Tao 2,3; Yin, Lin 2,3; An, Yu 5,6,7; Zhong, Jing 1; Hui, Hui 2,3; Tian, Jie 2,5,6,7
刊名	PHYSICS IN MEDICINE AND BIOLOGY
	2023-07-21
卷号	68 期号:14 页码:17
关键词	magnetic particle imaging reconstruction method inverse problem
ISSN号	0031-9155
DOI	10.1088/1361-6560/ace022
通讯作者	Liu, Jie(jieliu@bjtu.edu.cn) ; Hui, Hui(hui.hui@ia.ac.cn)
英文摘要	Objective. Here, we propose a dynamic residual Kaczmarz (DRK) method as an improved reconstruction method for magnetic particle imaging (MPI) to achieve a better reconstruction quality from high-noise signals. Approach. Based on the Kaczmarz (KZ) method, we introduced a residual vector to select parts of the low-noise equations for reconstruction. In each iteration, a low-noise subset was formulated based on the residual vector. Thus, the reconstruction converged to an accurate result with less noise. Main Results. To evaluate the performance of the proposed method, it was compared with classical Kaczmarz-type methods and state-of-the-art regularization models. The numerical simulation results demonstrate that the DRK method can achieve better reconstruction quality than all other comparison methods at similar noise levels. It can acquire a signal-to-background ratio (SBR) that is five times higher than that of classical Kaczmarz-type methods at a 5 dB noise level. Furthermore, the DRK method can acquire up to 0.7 structural similarity (SSIM) indicators at a 5 dB noise level when combined with the non-negative fused Least absolute shrinkage and selection operator (LASSO) regularization model. In addition, a real experiment based on the OpenMPI data set validated that the proposed DRK method can be applied to real data and perform well. Significance. The experimental results demonstrate that the proposed DRK method can significantly improve the reconstruction quality of MPI when the signals contain high noise. It has the potential to be applied to MPI instruments that contain high signal noise, such as human-sized MPI instruments. It is beneficial for expanding the biomedical applications of MPI technology.
资助项目	National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2017YFA0700403] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81827808] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81571836] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[KKA309004533] ; Beijing Natural Science Foundation[JQ22023] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2018167] ; Chinese Academy of Sciences Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703]
WOS关键词	RESOLUTION
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:001025383600001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association of the Chinese Academy of Sciences ; Chinese Academy of Sciences Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53687]
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	Liu, Jie; Hui, Hui
作者单位	1.Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100080, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 6.Beihang Univ, Sch Engn Med, Beijing 100191, Peoples R China 7.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Peng,Liu, Jie,Li, Yimeng,et al. Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging[J]. PHYSICS IN MEDICINE AND BIOLOGY,2023,68(14):17.
APA	Zhang, Peng.,Liu, Jie.,Li, Yimeng.,Zhu, Tao.,Yin, Lin.,...&Tian, Jie.(2023).Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging.PHYSICS IN MEDICINE AND BIOLOGY,68(14),17.
MLA	Zhang, Peng,et al."Dynamic residual Kaczmarz method for noise reducing reconstruction in magnetic particle imaging".PHYSICS IN MEDICINE AND BIOLOGY 68.14(2023):17.