Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging | |
Yusong Shen1; Zhang LW(张利文)2,3,4![]() ![]() ![]() | |
发表期刊 | Physics in Medicine & Biology
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2024-06 | |
页码 | 无 |
文章类型 | SCI |
摘要 | Objective. Magnetic Particle Imaging (MPI) is an emerging medical tomographic imaging modality that enables real-time imaging with high sensitivity and high spatial and temporal resolution. For the system matrix reconstruction method, the MPI reconstruction problem is an ill-posed inverse problem that is commonly solved using the Kaczmarz algorithm. However, the high computation time of the Kaczmarz algorithm, which restricts MPI reconstruction speed, has limited the development of potential clinical applications for real-time MPI. In order to achieve fast reconstruction in real-time MPI, we propose a greedy regularized block Kaczmarz method (GRBK) which accelerates MPI reconstruction. Approach. GRBK is composed of a greedy partition strategy for the system matrix, which enables preprocessing of the system matrix into well-conditioned blocks to facilitate the convergence of the block Kaczmarz algorithm, and a regularized block Kaczmarz algorithm, which enables fast and accurate MPI image reconstruction at the same time. Main results. We quantitatively evaluated our GRBK using simulation data from three phantoms at 20dB, 30dB, and 40dB noise levels. The results showed that GRBK can improve reconstruction speed by single orders of magnitude compared to the prevalent regularized Kaczmarz algorithm including Tikhonov regularization, the non-negative Fused Lasso, and wavelet-based sparse model. We also evaluated our method on OpenMPIData, which is real MPI data. The results showed that our GRBK is better suited for real-time MPI reconstruction than current state-of-the-art reconstruction algorithms in terms of reconstruction speed as well as image quality. Significance. Our proposed method is expected to be the preferred choice for potential applications of real-time MPI. |
语种 | 英语 |
七大方向——子方向分类 | 人工智能+医疗 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57471 |
专题 | 中国科学院分子影像重点实验室 |
作者单位 | 1.School of Computer Science and Engineering, Southeast University 2.CAS Key Laboratory of Molecular Imaging, CAS Institute of Automation 3.Beijing Key Laboratory of Molecular Imaging 4.University of Chinese Academy of Sciences 5.School of Engineering Medicine, Beihang University 6.Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of Chin 7.National Key Laboratory of Kidney Diseases |
推荐引用方式 GB/T 7714 | Yusong Shen,Zhang LW,Hui Zhang,et al. A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging[J]. Physics in Medicine & Biology,2024:无. |
APA | Yusong Shen.,Zhang LW.,Hui Zhang.,Yimeng Li.,Jing Zhao.,...&Hui Hui.(2024).A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging.Physics in Medicine & Biology,无. |
MLA | Yusong Shen,et al."A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging".Physics in Medicine & Biology (2024):无. |
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Shen et al_2024_Phys(1377KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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