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相关概念视频

Computed Tomography01:10

Computed Tomography

4.2K
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...
4.2K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.3K
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...
2.3K
X-ray Imaging01:24

X-ray Imaging

5.3K
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...
5.3K

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相关实验视频

Updated: May 24, 2025

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

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多个基质材料的图像重建与DECT中的数据增强.

Buxin Chen, Zheng Zhang, Dan Xia

    IEEE transactions on bio-medical engineering
    |March 3, 2025
    PubMed
    概括

    本研究介绍了一种用于双能CT (DECT) 的新算法,它可以准确地重建多个基图像和虚拟单色图像 (VMIs),即使是复杂的材料组成.

    科学领域:

    • 医疗成像医学成像
    • 计算成像技术的成像
    • 图像重建 图像的重建

    背景情况:

    • 双能CT (DECT) 成像在基础区域含有多种材料时会带来重建挑战.
    • 不知数的数量可能超过测量的数量,使准确的图像合成复杂化.

    研究的目的:

    • 在DECT.中开发准确和稳定的重建多个基图像和虚拟单色图像 (VMIs) 的算法.
    • 用最多 3 种基础材料来解决 DECT 中的重建挑战.

    主要方法:

    • 开发了一个基于优化的算法,利用体积保存 (VC) 约束.
    • 该VC约束增加数据,以克服DECT重建中的局限性.

    主要成果:

    • 使用模拟和真实数据进行的数值研究证明了多个基础图像和VMIs的准确和稳定的重建.
    • 该算法成功处理了多达3个基础材料的DECT案例.

    结论:

    • 利用VC约束可以在DECT中准确稳定地重建多个基础图像.
    • 开发的算法可在复杂的DECT场景中提供准确的VMI和对比度估计.

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