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Zhao J, Zhou J, Zhang L, Sun J. State Space Representation of Jiles-Atherton Hysteresis Model and Application for Closed-Loop Control. MATERIALS (BASEL, SWITZERLAND) 2024; 17:3695. [PMID: 39124358 PMCID: PMC11313527 DOI: 10.3390/ma17153695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024]
Abstract
Hysteresis is a fundamental characteristic of magnetic materials. The Jiles-Atherton (J-A) hysteresis model, which is known for its few parameters and clear physical interpretations, has been widely employed in simulating hysteresis characteristics. To better analyze and compute hysteresis behavior, this study established a state space representation based on the primitive J-A model. First, based on the five fundamental equations of the J-A model, a state space representation was established through variable substitution and simplification. Furthermore, to address the singularity problem at zero crossings, local linearization was obtained through an approximation method based on the actual physical properties. Based on these, the state space model was implemented using the S-function. To validate the effectiveness of the state space model, the hysteresis loops were obtained through COMSOL finite element software and tested on a permalloy toroidal sample. The particle swarm optimization (PSO) method was used for parameter identification of the state space model, and the identification results show excellent agreement with the simulation and test results. Finally, a closed-loop control system was constructed based on the state space model, and trajectory tracking experiments were conducted. The results verify the feasibility of the state space representation of the J-A model, which holds significant practical implications in the development of magnetically shielded rooms, the suppression of magnetic interference in cold atom clocks, and various other applications.
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Affiliation(s)
- Jiye Zhao
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China;
- Hangzhou Institute of National Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310028, China
| | - Jiqiang Zhou
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, Beihang University, Beijing 100191, China;
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
| | - Lu Zhang
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China;
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, Beihang University, Beijing 100191, China;
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
| | - Jinji Sun
- School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China;
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, Beihang University, Beijing 100191, China;
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Shen Y, Zhang L, Zhang H, Li Y, Zhao J, Tian J, Yang G, Hui H. A greedy regularized block Kaczmarz method for accelerating reconstruction in magnetic particle imaging. Phys Med Biol 2024; 69:155004. [PMID: 38862003 DOI: 10.1088/1361-6560/ad56f1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
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 20 dB, 30 dB, and 40 dB 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.
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Affiliation(s)
- Yusong Shen
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Liwen Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100080, People's Republic of China
| | - Hui Zhang
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beijing, People's Republic of China
| | - Yimeng Li
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beijing, People's Republic of China
| | - Jing Zhao
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
| | - Jie Tian
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of China
| | - Guanyu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing 100080, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of China
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Shen Y, Zhang L, Hui H, Guo L, Wang T, Yang G, Tian J. A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 252:108250. [PMID: 38815547 DOI: 10.1016/j.cmpb.2024.108250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 05/08/2024] [Accepted: 05/24/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Magnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. METHODS We propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. RESULTS The experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. CONCLUSIONS We developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.
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Affiliation(s)
- Yusong Shen
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
| | - Liwen Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China
| | - Lishuang Guo
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Tan Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Guanyu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
| | - Jie Tian
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China; School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China.
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Shan S, Zhang C, Yin L, Yang X, Yu D, Qi Y, Li M, Wildgruber M, Du Y, Tian J, Ma X. Combination of time domain-system matrix and x-space methods to reconstruct magnetic particle images with isotropic resolution. Phys Med Biol 2024; 69:035004. [PMID: 38168021 DOI: 10.1088/1361-6560/ad19f0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective. Imaging of superparamagnetic iron oxide nanoparticles based on their non-linear response to alternating magnetic fields shows promise for imaging cells and vasculature in healthy and diseased tissue. Such imaging can be achieved through x-space reconstruction typically along a unidirectional Cartesian trajectory, which rapidly convolutes the particle distribution with a 'anisotropic blurring' point spread function (PSF), leading to images with anisotropic resolution.Approach. Here we propose combining the time domine-system matrix and x-space reconstruction methods into a forward model, where the output of the forward model is the PSF-blurred x-space reconstructed image. We then treat the blur as an inverse problem solved by Kaczmarz iteration.Main results. After we have proposed the method optimization, the normal resolution of simulation and device images has been increased from 3.5 mm and 5.25 mm to 1.5 mm and 3.25 mm, which has reached the level in the tangential resolution. Quantitative indicators of image quality such as PSNR and SSIM have also been greatly improved.Significance. Simulation and imaging of real phantoms indicate that our approach provides better isotropic resolution and image quality than the x-space method alone or other methods for removing PSF blur. Using our proposed method to optimize the image quality of x-space reconstructed images using unidirectional Cartesian trajectories, it will promote the clinical application of MPI in the future.
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Affiliation(s)
- Shihao Shan
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Chenglong Zhang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Lin Yin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, People's Republic of China
| | - Xiaoli Yang
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People's Republic of China
| | - Yafei Qi
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People's Republic of China
| | - Min Li
- Department of Nuclear Medicine, 960 Hospital of PLA, No. 25, Shifan Road, Jinan, Shandong 250031, People's Republic of China
| | - Moritz Wildgruber
- Department of Radiology, University Hospital, LMU Munich, Munich D-81337, Germany
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, People's Republic of China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, People's Republic of China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, People's Republic of China
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Peng Z, Yin L, Sun Z, Liang Q, Ma X, An Y, Tian J, Du Y. DERnet: a deep neural network for end-to-end reconstruction in magnetic particle imaging. Phys Med Biol 2023; 69:015002. [PMID: 38064750 DOI: 10.1088/1361-6560/ad13cf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023]
Abstract
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.
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Affiliation(s)
- Zhengyao Peng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Lin Yin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Zewen Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, Shandon, People's Republic of China
| | - Yu An
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, People's Republic of China
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Shang Y, Liu J, Wang Y. Fusion Networks of CNN and Transformer with Channel Attention for Accurate Tumor Imaging in Magnetic Particle Imaging. BIOLOGY 2023; 13:2. [PMID: 38275723 PMCID: PMC11154287 DOI: 10.3390/biology13010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024]
Abstract
BACKGROUND Magnetic Particle Imaging (MPI) is an emerging molecular imaging technique. However, since X-space reconstruction ignores system properties, it can lead to blurring of the reconstructed image, posing challenges for accurate quantification. To address this issue, we propose the use of deep learning to remove the blurry artifacts; (2) Methods: Our network architecture consists of a combination of Convolutional Neural Network (CNN) and Transformer. The CNN utilizes convolutional layers to automatically extract pixel-level local features and reduces the size of feature maps through pooling layers, effectively capturing local information within the images. The Transformer module is responsible for extracting contextual features from the images and efficiently capturing long-range dependencies, enabling a more effective modeling of global features in the images. By combining the features extracted by both CNN and Transformer, we capture both global and local features simultaneously, thereby improving the quality of reconstructed images; (3) Results: Experimental results demonstrate that the network effectively removes blurry artifacts from the images, and it exhibits high accuracy in precise tumor quantification. The proposed method shows superior performance over the state-of-the-art methods; (4) Conclusions: This bears significant implications for the image quality improvement and clinical application of MPI technology.
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Affiliation(s)
- Yaxin Shang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
| | - Yueqi Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100080, China
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Shen Y, Zhang L, Shang Y, Jia G, Yin L, Zhang H, Tian J, Yang G, Hui H. An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction. Phys Med Biol 2023; 68:245016. [PMID: 37890461 DOI: 10.1088/1361-6560/ad078d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 10/27/2023] [Indexed: 10/29/2023]
Abstract
Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods.Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM.Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts.Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.
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Affiliation(s)
- Yusong Shen
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Liwen Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Yaxin Shang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, People's Republic of China
| | - Guang Jia
- School of Computer Science and Technology, Xidian University, Xi'an Shaanxi, People's Republic of China
| | - Lin Yin
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Hui Zhang
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beijing, People's Republic of China
| | - Jie Tian
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China
- School of Engineering Medicine, Beihang University, Beijing, People's Republic of China
- Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology of the People's Republic of China, Beijing, People's Republic of China
| | - Guanyu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
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8
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Shang Y, Liu J, Liu Y, Zhang B, Wu X, Zhang L, Tong W, Hui H, Tian J. Anisotropic edge-preserving network for resolution enhancement in unidirectional Cartesian magnetic particle imaging. Phys Med Biol 2023; 68. [PMID: 36689774 DOI: 10.1088/1361-6560/acb584] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/23/2023] [Indexed: 01/24/2023]
Abstract
Objective. Magnetic particle imaging (MPI) is a novel imaging modality. It is crucial to acquire accurate localization of the superparamagnetic iron oxide nanoparticles distributions in MPI. However, the spatial resolution of unidirectional Cartesian trajectory MPI exhibits anisotropy, which blurs the boundaries of MPI images and makes precise localization difficult. In this paper, we propose an anisotropic edge-preserving network (AEP-net) to alleviate the anisotropic resolution of MPI.Methods. AEP-net resolve the resolution anisotropy by constructing an asymmertic convolution. To recover the edge information, we design the uncertainty region module. In addition, we evaluated the performance of the proposed AEP-net model by using simulations and experimental data.Results. The results show that the AEP-net model alleviates the anisotropy of the unidirectional Cartesian trajectory and preserves edge details in the MPI image. By comparing the visualization results and the metrics, we demonstrate that our method is superior to other methods.Significance. The proposed method produces accurate visualization in unidirectional Cartesian devices and promotes accurate quantization, which promote the biomedical applications using MPI.
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Affiliation(s)
- Yaxin Shang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People's Republic of China
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, People's Republic of China
| | - Yanjun Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, People's Republic of China
| | - Bo Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, People's Republic of China
| | - Xiangjun Wu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, People's Republic of China
| | - Liwen Zhang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, People's Republic of China.,The University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Wei Tong
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, 100036, People's Republic of China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, People's Republic of China.,The University of Chinese Academy of Sciences, Beijing, 100080, People's Republic of China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, People's Republic of China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China
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