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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 PMCID: PMC11324856 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.
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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
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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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Zhang J, Wei Z, Wu X, Shang Y, Tian J, Hui H. Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework. Comput Biol Med 2023; 165:107461. [PMID: 37708716 DOI: 10.1016/j.compbiomed.2023.107461] [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: 05/18/2023] [Revised: 08/27/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.
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Affiliation(s)
- Jiaxin Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Key Laboratory of Molecular Imaging, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Zechen Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Key Laboratory of Molecular Imaging, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xiangjun Wu
- Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China; School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yaxin Shang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Key Laboratory of Molecular Imaging, Beijing, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, China; School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Key Laboratory of Molecular Imaging, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
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