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Cheng Z, Sun Z, Wang J, Jia K. Magneto-acousto-electrical tomography using nonlinearly frequency-modulated ultrasound. Phys Med Biol 2024; 69:085014. [PMID: 38422542 DOI: 10.1088/1361-6560/ad2ee5] [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: 11/10/2023] [Accepted: 02/29/2024] [Indexed: 03/02/2024]
Abstract
Objective. In this study, nonlinearly frequency-modulated (NLFM) ultrasound was applied to magneto-acousto-electrical tomography (MAET) to increase the dynamic range of detection.Approach. Generation of NLFM signals using window function method-based on the principle of stationary phase-and piecewise linear frequency modulation method-based on the genetic algorithm-was discussed. The MAET experiment systems using spike, linearly frequency-modulated (LFM), or NLFM pulse stimulation were constructed, and three groups of MAET experiments on saline agar phantom samples were carried out to verify the performance-respectively the sensitivity, the dynamic range, and the longitudinal resolution of detection-of MAET using NLFM ultrasound in comparison to that using LFM ultrasound. Based on the above experiments, a pork sample was imaged by ultrasound imaging method, spike MAET method, LFM MAET method, and NLFM MAET method, to compare the imaging accuracy.Main results. The experiment results showed that, through sacrificing very little main-lobe width of pulse compression or equivalently the longitudinal resolution, the MAET using NLFM ultrasound achieved higher signal-to-interference ratio (and therefore higher detection sensitivity), lower side-lobe levels of pulse compression (and therefore larger dynamic range of detection), and large anti-interference capability, compared to the MAET using LFM ultrasound.Significance. The applicability of the MAET using NLFM ultrasound was proved in circumferences where sensitivity and dynamic range of detection were mostly important and slightly lower longitudinal resolution of detection was acceptable. The study furthered the scheme of using coded ultrasound excitation toward the clinical application of MAET.
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Affiliation(s)
- Zhizhuo Cheng
- College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Zhishen Sun
- College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Jianfei Wang
- Beijing Key Laboratory of Nonlinear Vibrations and Strength of Mechanical Structures, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Kebin Jia
- College of Information and Communication Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, People's Republic of China
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Sun T, Yu M, Yu L, Deng D, Chen M, Lin H, Chen S, Chang C, Chen X. Iterative Reconstruction Algorithms in Magneto-Acousto-Electrical Computed Tomography (MAE-CT) for Image Quality Improvement. IEEE Trans Biomed Eng 2024; 71:669-678. [PMID: 37698962 DOI: 10.1109/tbme.2023.3314617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Magneto-acousto-electrical computed tomography (MAE-CT) is a recently developed rotational magneto-acousto-electrical tomography (MAET) method, which can map the conductivity parameter of tissues with high spatial resolution. Since the imaging mode of MAE-CT is similar to that of CT, the reconstruction algorithms for CT are possible to be adopted for MAE-CT. Previous studies have demonstrated that the filtered back-projection (FBP) algorithm, which is one of the most common CT reconstruction algorithms, can be used for MAE-CT reconstruction. However, FBP has some inherent shortcomings of being sensitive to noise and non-uniform distribution of views. In this study, we introduced iterative reconstruction (IR) method in MAE-CT reconstruction and compared its performance with that of the FBP. The numerical simulation, the phantom, and in vitro experiments were performed, and several IR algorithms (ART, SART, SIRT) were used for reconstruction. The results show that the images reconstructed by the FBP and IR are similar when the data is noise-free in the simulation. As the noise level increases, the images reconstructed by SART and SIRT are more robust to the noise than FBP. In the phantom experiment, noise and some stripe artifacts caused by the FBP are removed by SART and SIRT algorithms. In conclusion, the IR method used in CT is applicable in MAE-CT, and it performs better than FBP, which indicates that the state-of-the-art achievements in the CT algorithm can also be adopted for the MAE-CT reconstruction in the future.
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Lu Y, Fan K, Yuan J, Chen Y, Ge Y, Tao C, Liu X. Free scan real time 3D ultrasound imaging with shading artefacts removal. ULTRASONICS 2023; 135:107091. [PMID: 37515837 DOI: 10.1016/j.ultras.2023.107091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/31/2023]
Abstract
Ultrasound imaging (USI) is a widely adopted imaging method in clinical diagnosis owing to its low cost, convenience, and safety. However, due to the complex acoustic attenuation, two-dimensional (2D) USI lacks the capability to achieve a clear imaging result when the target is shaded by high echo tissues. This paper proposes a three-dimensional (3D) free-scan real-time ultrasound imaging (FRUSI) method. By integrating 2D ultrasound image sequences around the region of interest (ROI) with a real-time and spatially accurate probe tracking method, the proposed FRUSI system provides clear and accurate ultrasound images for medical study. The experiment results on reconstruction precision and accuracy show the potential ability of our proposed system to provide high-quality 3D ultrasound imaging. Moreover, previously shaded targets can be discerned clearly in the same scan plane in both phantom studies and in vivo studies on the human finger joint. The performance of the proposed FRUSI system has demonstrated its potential value for clinical diagnosis to provide high ultrasound imaging quality and rich details in spatial information. Due to the convenient setup, the FRUSI system might potentially be expanded to other ultrasound imaging modalities.
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Affiliation(s)
- Yanchen Lu
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Kai Fan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Jie Yuan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China.
| | - Ying Chen
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210046, China
| | - Chao Tao
- School of Physics, Nanjing University, Nanjing 210046, China
| | - Xiaojun Liu
- School of Physics, Nanjing University, Nanjing 210046, China
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Bu S, Li Y, Ren W, Liu G. ARU-DGAN: A dual generative adversarial network based on attention residual U-Net for magneto-acousto-electrical image denoising. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19661-19685. [PMID: 38052619 DOI: 10.3934/mbe.2023871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Magneto-Acousto-Electrical Tomography (MAET) is a multi-physics coupling imaging modality that integrates the high resolution of ultrasound imaging with the high contrast of electrical impedance imaging. However, the quality of images obtained through this imaging technique can be easily compromised by environmental or experimental noise, thereby affecting the overall quality of the imaging results. Existing methods for magneto-acousto-electrical image denoising lack the capability to model local and global features of magneto-acousto-electrical images and are unable to extract the most relevant multi-scale contextual information to model the joint distribution of clean images and noise images. To address this issue, we propose a Dual Generative Adversarial Network based on Attention Residual U-Net (ARU-DGAN) for magneto-acousto-electrical image denoising. Specifically, our model approximates the joint distribution of magneto-acousto-electrical clean and noisy images from two perspectives: noise removal and noise generation. First, it transforms noisy images into clean ones through a denoiser; second, it converts clean images into noisy ones via a generator. Simultaneously, we design an Attention Residual U-Net (ARU) to serve as the backbone of the denoiser and generator in the Dual Generative Adversarial Network (DGAN). The ARU network adopts a residual mechanism and introduces a linear Self-Attention based on Cross-Normalization (CNorm-SA), which is proposed in this paper. This design allows the model to effectively extract the most relevant multi-scale contextual information while maintaining high resolution, thereby better modeling the local and global features of magneto-acousto-electrical images. Finally, extensive experiments on a real-world magneto-acousto-electrical image dataset constructed in this paper demonstrate significant improvements in preserving image details achieved by ARU-DGAN. Furthermore, compared to the state-of-the-art competitive methods, it exhibits a 0.3 dB increase in PSNR and an improvement of 0.47% in SSIM.
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Affiliation(s)
- Shuaiyu Bu
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- State Grid Beijing Electric Power Company, Beijing 100031, China
| | - Yuanyuan Li
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenting Ren
- Department of Radiation Oncology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Guoqiang Liu
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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Zhang H, Meng Z, Ru J, Meng Y, Wang K. Application and prospects of AI-based radiomics in ultrasound diagnosis. Vis Comput Ind Biomed Art 2023; 6:20. [PMID: 37828411 PMCID: PMC10570254 DOI: 10.1186/s42492-023-00147-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.
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Affiliation(s)
- Haoyan Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zheling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jinyu Ru
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Yaqing Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China.
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