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Rezaei B, Tay ZW, Mostufa S, Manzari ON, Azizi E, Ciannella S, Moni HEJ, Li C, Zeng M, Gómez-Pastora J, Wu K. Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications. NANOSCALE 2024; 16:11802-11824. [PMID: 38809214 DOI: 10.1039/d4nr01195c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Recent advancements in medical imaging have brought forth various techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound, each contributing to improved diagnostic capabilities. Most recently, magnetic particle imaging (MPI) has become a rapidly advancing imaging modality with profound implications for medical diagnostics and therapeutics. By directly detecting the magnetization response of magnetic tracers, MPI surpasses conventional imaging modalities in sensitivity and quantifiability, particularly in stem cell tracking applications. Herein, this comprehensive review explores the fundamental principles, instrumentation, magnetic nanoparticle tracer design, and applications of MPI, offering insights into recent advancements and future directions. Novel tracer designs, such as zinc-doped iron oxide nanoparticles (Zn-IONPs), exhibit enhanced performance, broadening MPI's utility. Spatial encoding strategies, scanning trajectories, and instrumentation innovations are elucidated, illuminating the technical underpinnings of MPI's evolution. Moreover, integrating machine learning and deep learning methods enhances MPI's image processing capabilities, paving the way for more efficient segmentation, quantification, and reconstruction. The potential of superferromagnetic iron oxide nanoparticle chains (SFMIOs) as new MPI tracers further advanced the imaging quality and expanded clinical applications, underscoring the promising future of this emerging imaging modality.
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Affiliation(s)
- Bahareh Rezaei
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Zhi Wei Tay
- National Institute of Advanced Industrial Science and Technology (AIST), Health and Medical Research Institute, Tsukuba, Ibaraki 305-8564, Japan
| | - Shahriar Mostufa
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Omid Nejati Manzari
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Ebrahim Azizi
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Stefano Ciannella
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Hur-E-Jannat Moni
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Changzhi Li
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
| | - Minxiang Zeng
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | | | - Kai Wu
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA.
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He J, Li Y, Zhang P, Hui H, Tian J. A fused LASSO operator for fast 3D magnetic particle imaging reconstruction. Phys Med Biol 2024; 69:135002. [PMID: 38815602 DOI: 10.1088/1361-6560/ad524b] [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: 01/23/2024] [Accepted: 05/30/2024] [Indexed: 06/01/2024]
Abstract
Objective.Magnetic particle imaging (MPI) is a promising imaging modality that leverages the nonlinear magnetization behavior of superparamagnetic iron oxide nanoparticles to determine their concentration distribution. Previous optimization models with multiple regularization terms have been proposed to achieve high-quality MPI reconstruction, but these models often result in increased computational burden, particularly for dense gridding 3D fields of view. In order to achieve faster reconstruction speeds without compromising reconstruction quality, we have developed a novel fused LASSO operator, total sum-difference (TSD), which effectively captures the sparse and smooth priors of MPI images.Methods.Through an analysis-synthesis equivalence strategy and a constraint smoothing strategy, the TSD regularized model was solved using the fast iterative soft-thresholding algorithm (FISTA). The resulting reconstruction method, TSD-FISTA, boasts low computational complexity and quadratic convergence rate over iterations.Results.Experimental results demonstrated that TSD-FISTA required only 10% and 37% of the time to achieve comparable or superior reconstruction quality compared to commonly used fused LASSO-based alternating direction method of multipliers and Tikhonov-based algebraic reconstruction techniques, respectively.Significance.TSD-FISTA shows promise for enabling real-time 3D MPI reconstruction at high frame rates for large fields of view.
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Affiliation(s)
- Jie He
- School of Engineering Medicine and 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 100191, People's Republic of China
| | - Yimeng Li
- School of Engineering Medicine and 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 100191, People's Republic of China
| | - Peng Zhang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, People's Republic of China
| | - Hui Hui
- 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 100049, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of China
| | - Jie Tian
- School of Engineering Medicine and 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 100191, People's Republic of China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- National Key Laboratory of Kidney Diseases, Beijing 100853, People's Republic of 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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Shi G, Yin L, An Y, Li G, Zhang L, Bian Z, Chen Z, Zhang H, Hui H, Tian J. Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3639-3650. [PMID: 37471193 DOI: 10.1109/tmi.2023.3297173] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.
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Zhao Y, Li S, He X, Yu J, Zhang L, Zhang H, Wei D, Wang B, Li J, Guo H, He X. Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy. BIOMEDICAL OPTICS EXPRESS 2023; 14:5298-5315. [PMID: 37854546 PMCID: PMC10581805 DOI: 10.1364/boe.498092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 10/20/2023]
Abstract
Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.
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Affiliation(s)
- Yizhe Zhao
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Shuangchen Li
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Lizhi Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Heng Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - De Wei
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Beilei Wang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jintao Li
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
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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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