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Yang K, Li Q, Liu H, Zeng Q, Cai D, Xu J, Zhou Y, Tsui PH, Zhou X. Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks. Phys Med Biol 2024; 69:075006. [PMID: 38382109 DOI: 10.1088/1361-6560/ad2b95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interference generated by HIFU irradiation overwhelms the B-mode monitoring images, compromising monitoring effectiveness. This study aims to overcome this problem using a one-dimensional (1D) deep convolutional neural network.Approach. U-Net-based networks have been proven to be effective in image reconstruction and denoising, and the two-dimensional (2D) U-Net has already been investigated for suppressing HIFU interference in ultrasound monitoring images. In this study, we propose that the one-dimensional (1D) convolution in U-Net-based networks is more suitable for removing HIFU artifacts and can better recover the contaminated B-mode images compared to 2D convolution.Ex vivoandinvivoHIFU experiments were performed on a clinically equivalent ultrasound-guided HIFU platform to collect image data, and the 1D convolution in U-Net, Attention U-Net, U-Net++, and FUS-Net was applied to verify our proposal.Main results.All 1D U-Net-based networks were more effective in suppressing HIFU interference than their 2D counterparts, with over 30% improvement in terms of structural similarity (SSIM) to the uncontaminated B-mode images. Additionally, 1D U-Nets trained usingex vivodatasets demonstrated better generalization performance ininvivoexperiments.Significance.These findings indicate that the utilization of 1D convolution in U-Net-based networks offers great potential in addressing the challenges of monitoring in ultrasound-guided HIFU systems.
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Affiliation(s)
- Kun Yang
- School of Microelectronics, Tianjin University, Tianjin, People's Republic of China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin, People's Republic of China
| | - Hengxin Liu
- School of Microelectronics, Tianjin University, Tianjin, People's Republic of China
| | - Qingxuan Zeng
- School of Microelectronics, Tianjin University, Tianjin, People's Republic of China
| | - Dejia Cai
- The State Key Laboratory of Ultrasound Engineering in Medicine, College of Biomedical Engineering, Chongqing Medical University, People's Republic of China
| | - Jiahong Xu
- The State Key Laboratory of Ultrasound Engineering in Medicine, College of Biomedical Engineering, Chongqing Medical University, People's Republic of China
| | - Yingying Zhou
- The State Key Laboratory of Ultrasound Engineering in Medicine, College of Biomedical Engineering, Chongqing Medical University, People's Republic of China
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Research Center for Radiation Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Xiaowei Zhou
- The State Key Laboratory of Ultrasound Engineering in Medicine, College of Biomedical Engineering, Chongqing Medical University, People's Republic of China
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