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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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利用化学交联的微泡集群使用深度学习进行超声波对比成像.

Teja Pathour1,2, Ghazal Rastegar1, Shashank R Sirsi1,2

  • 1University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States.

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概括

机器学习准确地识别了化学交联的微泡集群 (CCMC) 的独特声学特征. 这提高了对比剂在超声波成像中的检测和定位,特别是在超分辨率应用中.

关键词:
检测异常检测异常检测集成的微气泡聚集在一起.对比剂对比剂是一种对比剂.与对比度增强的超声波成像成像技术深度学习是一种深度学习.机器学习是机器学习.超声波超声波是指超声波的使用.

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科学领域:

  • 生物医学工程 生物医学工程
  • 声学物理 声学物理
  • 机器学习 机器学习

背景情况:

  • 微气泡集群 (CCMC) 是具有独特声学特性的先进对比剂.
  • 在有针对性的超声波应用中,区分CCMC与单个微气泡 (MB) 是至关重要的.
  • 机器学习 (ML) 为分析复杂的声学数据提供了新的方法.

研究的目的:

  • 调查和分离CCMC的独特声学特性.
  • 应用ML,特别是基于自编码器的异常检测,用于CCMC声学分析.
  • 在超声波成像中提高对比剂的检测和定位.

主要方法:

  • 使用无铜点击化学合成的CCMCs.
  • 使用临床传感器对CCMC和个人MB进行声学分析.
  • 处理用于ML模型训练和测试 (异常检测) 的射频数据.

主要成果:

  • 异常检测模型成功识别了CCMCs的独特声学特征.
  • 频率分析显示CCMC声信号的振幅和能量更高,表明凝聚.
  • 对照实验证实了该模型在区分聚类和非聚类MB中的特异性.

结论:

  • 基于ML的异常检测对于识别CCMC声学特征是可行的.
  • CCMC 呈现较高的声幅,有利于对比剂检测.
  • 这种方法有望改善超声波成像,特别是超分辨率应用.