CASIA OpenIR  > 中国科学院分子影像重点实验室
A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging
Yusong Shen1; Zhang LW(张利文)2,3,4; Hui Zhang5,6; Yimeng Li5,6; Jing Zhao5; Jie Tian1,2,3,5,7; Guanyu Yang1; Hui Hui2,3,4,7
Source PublicationPhysics in Medicine & Biology

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.

Sub direction classification人工智能+医疗
planning direction of the national heavy laboratoryAI For Science
Paper associated data
Document Type期刊论文
Affiliation1.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
Recommended Citation
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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