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相关概念视频

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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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
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography
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稀疏视图同步龙X射线断层图形重建与基于学习的阴影图合成.

Chang Chieh Cheng1, Ming Hsuan Chiang2, Chao Hong Yeh3

  • 1Information Technology Service Center, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.

Journal of synchrotron radiation
|October 18, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用卷积神经网络 (CNN) 的深度学习方法,从稀疏视图投影中重建高质量的X射线断层图像,降低辐射剂量和成本.

关键词:
深度学习是一种深度学习.阴影图的合成方法稀疏视图计算机断层扫描.同步龙X射线计算机断层扫描.查看插曲对应的视图

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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 计算生物学 计算生物学
  • 材料科学 材料科学 材料科学

背景情况:

  • 同步X射线显微镜使得对断层扫描的高分辨率成像成为可能.
  • 获取详细断层扫描的众多投影是耗时的,昂贵的,并增加了辐射暴露.
  • 现有的稀疏获取方法通常会产生带有文物和噪音的图像.

研究的目的:

  • 开发一种基于深度学习的方法,用于使用稀疏视图X射线投影的断层重建.
  • 为了应对与密集X射线成像相关的时间消耗,高成本和辐射剂量的挑战.
  • 从有限的投影数据提高断层图像重建的质量.

主要方法:

  • 一个卷积神经网络 (CNN) 插入稀疏的X射线投影,以创建一个密集的sinogram.
  • 第二个CNN用于在重建的sinogram中纠正错误.
  • 转移学习被用来调整一个在Drosophila数据上训练的模型,以改善小鼠断层扫描的重建.

主要成果:

  • 提出的深度学习方法成功地从稀疏视图投影生成了高质量的断层扫描图像.
  • 这种方法在Drosophila和小鼠数据集上都表现出有效性.
  • 转移学习显著提高了较小小鼠数据集的重建质量.

结论:

  • 深度学习,特别是CNN,为高质量的稀疏视图断层扫描重建提供了可行的解决方案.
  • 这种方法可以大大减少基于同步基X射线显微镜的成像时间,成本和辐射剂量.
  • 转移学习的应用可以提高模型在较小或相关数据集上的性能.