基于深度学习的Golay编码激发用于超声波成像
Suntae Hwang1, Jinwoo Kim2, Eunji Lee1
1Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea.
Ultrasonics
|November 2, 2025
概括
这项研究介绍了Golay-Net,这是一种用于超声波成像的深度学习框架. 通过合成超声波回声信号,Golay-Net可以提高信号噪声比和成像深度,而不会降低率.
科学领域:
- 医疗成像医学成像
- 超声波技术 超声波技术 超声波技术
- 人工智能的人工智能
背景情况:
- 传统的超声波成像使用短脉冲,受到信号衰减和透率降低的限制.
- 戈莱编码激发提高了SNR和深度,但由于双传输,将率减半.
研究的目的:
- 开发一个深度学习框架,以克服Golay编码超声波激发的率限制.
- 为了提高超声波成像深度和SNR,而不会影响实时性能.
主要方法:
- 开发了一个新的深度学习框架,Golay-Net,基于一个1-D U-Net架构.
- 戈莱-网通过修改范围侧叶相,从代码A的回声信号中合成代码B的回声信号.
主要成果:
- 戈莱-网成功地合成了与B相关的回声信号的代码,并且具有高保真度.
- 重建的超声波图像显示了增强的SNR和成像深度.
- 提出的方法保持了原来的率.
结论:
- 戈莱网络为戈莱编码超声波中的成像性能和率之间的权衡提供了解决方案.
- 这种深度学习方法显著提高了超声波诊断能力.
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