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相关概念视频

Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

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As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
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相关实验视频

Updated: Apr 7, 2026

Three-dimensional Optical-resolution Photoacoustic Microscopy
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基于学习的声速估计和对线性阵列光声成像的偏差校正.

Mengjie Shi1, Tom Vercauteren1, Wenfeng Xia1

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, United Kingdom.

Photoacoustics
|December 13, 2024
PubMed
概括

这项研究引入了一个深度学习框架,以准确估计声速 (SoS),以改进光声学 (PA) 成像. 该方法纠正了异常,提高了临床环境中的图像质量.

科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 医学物理 医学物理
  • 人工智能的人工智能

背景情况:

  • 光声学 (PA) 图像重建需要准确的音速 (SoS) 数据,通常在软组织中假定是均的 (1540 m/s).
  • 不同质的SoS分布导致异常工件,降低PA图像质量并阻碍临床使用.
  • 现有的SoS校正方法通常需要复杂的硬件或冗长的算法,限制了临床翻译.

研究的目的:

  • 开发一个深度学习框架,用于在双模态PA/US成像中准确的SoS估计和偏差校正.
  • 将估计的SoS分布整合到PA图像重建中,以提高图像质量.
  • 在拟议框架内使用临床超声波探针.

主要方法:

  • 使用超声波通道数据,使用深度神经网络进行SoS估计.
  • 该框架涉及对数字幻影进行预培训,并通过物理幻影数据转移学习.
  • 使用来自双模系统的共同注册的PA和US图像.

主要成果:

  • 实现了SoS估计准确度,根平均平方误差为10.2m/s (数字) 和15.2m/s (物理) 幻影.
  • 在PA图像重建质量方面显著改善,结构相似度指数高达0.88,而0.69.
  • 在人类志愿者研究中,报告了PA图像信号与噪声比的1.2倍改善.
关键词:
异常纠正的纠正异常纠正的纠正深度学习是一种深度学习.图像重建 图像重建照片声学成像成像技术声音估计速度的速度.超声波成像 超声波成像

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结论:

  • 拟议的深度学习框架有效地估计了SoS,并纠正了超级PA图像重建的误差.
  • 这种方法为在各种临床和临床前应用中增强PA成像提供了有价值的工具.
  • 该框架依赖临床超声波探针,这有助于潜在的临床转化.