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相关概念视频

Computed Tomography01:10

Computed Tomography

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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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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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相关实验视频

Updated: Jun 23, 2025

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography
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Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

Published on: May 27, 2008

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没有训练的极限少数视图断层扫描数据数据.

Gengsheng L Zeng1,2

  • 1Department of Computer Science, Utah Valley University, USA.

Biomedical journal of scientific & technical research
|June 17, 2024
PubMed
概括
此摘要是机器生成的。

从极端少视图断层扫描中重建图像是具有挑战性的. 本研究介绍了使用线性减弱系数作为额外的约束,显著提高图像重建质量超出目前的压缩传感方法.

关键词:
少数视图断层扫描 (Few-View Tomography) 是一个非常简单的技术.图像重建 图像的重建这是一个反向问题.代算法 代算法 代算法优化优化 优化优化总变化最小化 总变化最小化

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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相关实验视频

Last Updated: Jun 23, 2025

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography
08:51

Non-invasive 3D-Visualization with Sub-micron Resolution Using Synchrotron-X-ray-tomography

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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科学领域:

  • 医疗成像医学成像
  • 计算成像技术的成像
  • 应用数学 应用数学 应用数学

背景情况:

  • 极少数视图断层扫描涉及从不到10个投影视图重建图像.
  • 当前最先进的方法依赖于压缩传感 (CS) 技术,利用散射转换和总变异 (TV) 规范最小化.
  • 标准的CS方法往往不足以应对极少数视图场景中的严重数据限制.

研究的目的:

  • 调查结合额外的先前信息作为极少数视图断层扫描的限制的可行性和有效性.
  • 在投影数据极为有限的场景中提高图像重建质量.
  • 为了解决极端少视界条件下现有的压缩传感方法的局限性.

主要方法:

  • 该研究建议使用对成像对象的线性衰减系数的先前知识作为补充约束.
  • 这些已知的衰减值被整合到重建过程中,以指导解决方案.
  • 该方法使用计算机模拟传输断层扫描来评估该方法.

主要成果:

  • 将线性衰减系数作为额外的约束体现出了图像重建质量的显著改善.
  • 与传统的压缩传感技术相比,在极端少视野条件下,拟议的方法显示出更高的性能.
  • 计算机模拟证实了这些额外约束在改进断层扫描重建方面的实用性.

结论:

  • 利用先前对线性衰减系数的知识是克服极少数视图断层扫描挑战的可行策略.
  • 这种方法在投影数据严重有限时,为图像重建提供了实际增强.
  • 这些发现表明,在很难或不可能获得众多视图的应用中,改善成像能力是一个有希望的方向.