1
|
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.
Collapse
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
| |
Collapse
|
2
|
Quelhas KN, Henn MA, Farias R, Tew WL, Woods SI. GPU-accelerated parallel image reconstruction strategies for magnetic particle imaging. Phys Med Biol 2024; 69:135005. [PMID: 38843809 DOI: 10.1088/1361-6560/ad5510] [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: 12/26/2023] [Accepted: 06/06/2024] [Indexed: 06/25/2024]
Abstract
Objective. Image reconstruction is a fundamental step in magnetic particle imaging (MPI). One of the main challenges is the fact that the reconstructions are computationally intensive and time-consuming, so choosing an algorithm presents a compromise between accuracy and execution time, which depends on the application. This work proposes a method that provides both fast and accurate image reconstructions.Approach. Image reconstruction algorithms were implemented to be executed in parallel ingraphics processing units(GPUs) using the CUDA framework. The calculation of the model-based MPI calibration matrix was also implemented in GPU to allow both fast and flexible reconstructions.Main results. The parallel algorithms were able to accelerate the reconstructions by up to about6,100times in comparison to the serial Kaczmarz algorithm executed in the CPU, allowing for real-time applications. Reconstructions using the OpenMPIData dataset validated the proposed algorithms and demonstrated that they are able to provide both fast and accurate reconstructions. The calculation of the calibration matrix was accelerated by up to about 37 times.Significance. The parallel algorithms proposed in this work can provide single-frame MPI reconstructions in real time, with frame rates greater than 100 frames per second. The parallel calculation of the calibration matrix can be combined with the parallel reconstruction to deliver images in less time than the serial Kaczmarz reconstruction, potentially eliminating the need of storing the calibration matrix in the main memory, and providing the flexibility of redefining scanning and reconstruction parameters during execution.
Collapse
Affiliation(s)
- Klaus N Quelhas
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mark-Alexander Henn
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- University of Maryland, College Park, MD, United States of America
| | - Ricardo Farias
- Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Weston L Tew
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Solomon I Woods
- Physical Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Nigam S, Gjelaj E, Wang R, Wei GW, Wang P. Machine Learning and Deep Learning Applications in Magnetic Particle Imaging. J Magn Reson Imaging 2024. [PMID: 38358090 DOI: 10.1002/jmri.29294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
Collapse
Affiliation(s)
- Saumya Nigam
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Elvira Gjelaj
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, USA
| | - Rui Wang
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Guo-Wei Wei
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biochemistry and Molecular Biology, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Ping Wang
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| |
Collapse
|
5
|
Khorasani A, Shahbazi-Gahrouei D, Safari A. Recent Metal Nanotheranostics for Cancer Diagnosis and Therapy: A Review. Diagnostics (Basel) 2023; 13:diagnostics13050833. [PMID: 36899980 PMCID: PMC10000685 DOI: 10.3390/diagnostics13050833] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
In recent years, there has been an increasing interest in using nanoparticles in the medical sciences. Today, metal nanoparticles have many applications in medicine for tumor visualization, drug delivery, and early diagnosis, with different modalities such as X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), etc., and treatment with radiation. This paper reviews recent findings of recent metal nanotheranostics in medical imaging and therapy. The study offers some critical insights into using different types of metal nanoparticles in medicine for cancer detection and treatment purposes. The data of this review study were gathered from multiple scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up through the end of January 2023. In the literature, many metal nanoparticles are used for medical applications. However, due to their high abundance, low price, and high performance for visualization and treatment, nanoparticles such as gold, bismuth, tungsten, tantalum, ytterbium, gadolinium, silver, iron, platinum, and lead have been investigated in this review study. This paper has highlighted the importance of gold, gadolinium, and iron-based metal nanoparticles in different forms for tumor visualization and treatment in medical applications due to their ease of functionalization, low toxicity, and superior biocompatibility.
Collapse
Affiliation(s)
- Amir Khorasani
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
- Correspondence: ; Tel.: +98-31-37929095
| | - Arash Safari
- Department of Radiology, Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz 71439-14693, Iran
| |
Collapse
|
6
|
Jia G, Huang L, Wang Z, Liang X, Zhang Y, Zhang Y, Miao Q, Hu K, Li T, Wang Y, Xi L, Feng X, Hui H, Tian J. Gradient-Based Pulsed Excitation and Relaxation Encoding in Magnetic Particle Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3725-3733. [PMID: 35862339 DOI: 10.1109/tmi.2022.3193219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Magnetic particle imaging (MPI) is a radiation-free vessel- and target-imaging modality that can sensitively detect nanoparticles. A static magnetic gradient field, referred to as a selection field, is required in MPI to provide a field-free region (FFR) for spatial encoding. The image resolution of MPI is closely related to the size of the FFR, which is determined by the selection field gradient amplitude. Because of the limitations of existing gradient coil hardware, the image resolution of MPI cannot satisfy the clinical requirements of human in vivo imaging. Pulsed excitation has been confirmed to improve the image resolution of MPI by breaking down the 'relaxation wall.' This work proposes the use of a pulsed waveform magnetic gradient from magnetic resonance imaging to further improve the image resolution of MPI. Through alignment of the gradient direction along the field-free line (FFL), each location on the FFL is able to have a unique excitation field strength that generates a specific relaxation-induced decay signal. Through excitation of nanoparticles on the FFL with many gradient profiles, a high-resolution, one-dimensional (1D) image can be reconstructed on the FFL. For larger magnetic nanoparticles, simulation results revealed that a pulsed excitation field with a greater flat portion generates a 1D bar pattern phantom image with a higher correlation and spatial resolution. With parallel FFL and gradient coil movements, high-resolution, two-dimensional (2D) Shepp-Logan phantom and brain vessel maps were reconstructed through repetition of the spatially resolved measurement of magnetic nanoparticles on the FFL.
Collapse
|
7
|
Yin L, Li W, Du Y, Wang K, Liu Z, Hui H, Tian J. Recent developments of the reconstruction in magnetic particle imaging. Vis Comput Ind Biomed Art 2022; 5:24. [PMID: 36180612 PMCID: PMC9525566 DOI: 10.1186/s42492-022-00120-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/16/2022] [Indexed: 11/07/2022] Open
Abstract
Magnetic particle imaging (MPI) is an emerging molecular imaging technique with high sensitivity and temporal-spatial resolution. Image reconstruction is an important research topic in MPI, which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration distribution. MPI reconstruction primarily involves system matrix- and x-space-based methods. In this review, we provide a detailed overview of the research status and future research trends of these two methods. In addition, we review the application of deep learning methods in MPI reconstruction and the current open sources of MPI. Finally, research opinions on MPI reconstruction are presented. We hope this review promotes the use of MPI in clinical applications.
Collapse
Affiliation(s)
- Lin Yin
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Wei Li
- grid.258164.c0000 0004 1790 3548Medical Imaging Center, the First Affiliated Hospital, Jinan University, Guangdong, 510632 China
| | - Yang Du
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Kun Wang
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhenyu Liu
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Hui Hui
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Jie Tian
- grid.429126.a0000 0004 0644 477XCAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ,Beijing Key Laboratory of Molecular Imaging, Beijing, 100190 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China ,grid.64939.310000 0000 9999 1211Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100083 China
| |
Collapse
|
8
|
Simulation of reconstruction based on the system matrix for magnetic particle imaging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103171] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
9
|
Research of magnetic particle imaging reconstruction based on the elastic net regularization. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|