Academic Journal
Spatial signal distribution learning for high-resolution 3D system matrix calibration in magnetic particle imaging.
| Title: | Spatial signal distribution learning for high-resolution 3D system matrix calibration in magnetic particle imaging. |
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| Authors: | Miao Z; School of Computer Science and Engineering, Southeast University, Nanjing 211189, People's Republic of China., Zhang L; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China., Chen Z; School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China.; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing 100190, People's Republic of China., Tian J; School of Computer Science and Engineering, Southeast University, Nanjing 211189, People's Republic of China.; School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, People's Republic of China.; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing 100190, People's Republic of China., Yang G; School of Computer Science and Engineering, Southeast University, Nanjing 211189, People's Republic of China., Li Y; School of Computer Science and Engineering, Southeast University, Nanjing 211189, People's Republic of China.; Lab of Molecular Imaging and Translational Medicine (MITM), Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, People's Republic of China., Hui H; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China. |
| Source: | Physics in medicine and biology [Phys Med Biol] 2026 Aug 19; Vol. 71 (16). Date of Electronic Publication: 2026 Aug 19. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing |
| MeSH Terms: | Magnetic Particle Imaging*/methods , Imaging, Three-Dimensional*/methods , Signal-To-Noise Ratio* , Machine Learning*, Calibration |
| Abstract: | Objective.Three-dimensional (3D) high-resolution system matrices (HR-SMs) are essential for high-quality image reconstruction in magnetic particle imaging (MPI), but obtaining HR-SMs is time-consuming and costly. This study aims to develop a learning-based 3D SM calibration method to reduce the calibration workload and maintain reconstruction accuracy.Approach.We propose a spatial signal distribution learning method for fast calibration of 3D HR-SMs. Specifically, a channel-decoupled multi-path state space model (MPC-SSM) is designed. This method flattens the 3D SM into multiple complementary spatial sequences, and uses different traversal paths to capture anisotropy and long-range spatial dependencies. To improve efficiency, feature channels are divided into disjoint groups and assigned to path-specific SSMs, achieving efficient multi-path sequence modeling while reducing computational overhead.Main results.We evaluated this method on both simulated and real MPI datasets (including OpenMPI). The results show that underandupsampling, the normalized reconstruction error of MPC-SSM is lower than that of existing interpolation and deep learning methods, and the quality of downstream image reconstruction is improved.Significance.This work provides a scalable and practical solution for 3D HR-SM calibration and provides a general modeling strategy for structured 3D medical data. (© 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: calibration; magnetic particle imaging; state space models; system matrix |
| Entry Date(s): | Date Created: 20260709 Date Completed: 20260819 Latest Revision: 20260819 |
| Update Code: | 20260820 |
| DOI: | 10.1088/1361-6560/ae88ae |
| PMID: | 42425149 |
| Database: | MEDLINE |
| ISSN: | 1361-6560 |
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| DOI: | 10.1088/1361-6560/ae88ae |