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相关概念视频

Computed Tomography01:10

Computed Tomography

6.2K
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...
6.2K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

50
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...
50
Positron Emission Tomography01:29

Positron Emission Tomography

5.5K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
5.5K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.5K
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...
2.5K

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相关实验视频

Updated: Sep 11, 2025

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
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Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

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DCT-UNet:用于扩散相关性断层扫描的UNet架构.

Yulong Li, Dmytro Nikolaienko, Jihui Wang

    Optics express
    |August 13, 2025
    PubMed
    概括

    这项研究介绍了DCT-UNet,这是一种深度学习模型,用于使用扩散相关性断层扫描 (DCT) 改进组织血流成像. DCT-UNet提高了血液流量指数 (BFI) 重建的准确性和稳定性,克服了传统方法的局限性.

    科学领域:

    • 生物医学光学 生物医学光学
    • 医疗成像医学成像
    • 机器学习 机器学习

    背景情况:

    • 扩散相关性断层扫描 (DCT) 是一种用于评估组织血流的光学成像技术.
    • 传统的血液流量指数 (BFI) 重建方法面临的挑战是由于错误的数学问题,影响准确性和稳定性.
    • 现有的算法依赖于光电场时间自相关函数,这些函数对噪声和数据稀疏性敏感.

    研究的目的:

    • 为增强DCT图像重建开发一个深度学习框架.
    • 为了在光学信号和血液流动断层图像之间建立一个强大的映射.
    • 克服传统DCT重建方法的局限性,使得血液流动成像更快,更准确.

    主要方法:

    • 提出了一个新的UNet架构,称为DCT-UNet,包含可变形卷曲和封闭单元.
    • 在DCT-UNet使用一个组聚桥 (GAB) 改进的编码器解码器连接.
    • 来自UNet++的深度监督被整合到多尺度面具生成中,增强了损失函数和GAB输入.

    主要成果:

    • 在计算机模拟和幻影实验中,DCT-UNet网络展示了卓越的准确性和稳定性.
    • 深度学习方法有效地解决了DCT重建中固有的不良问题.
    • 拟议的方法允许快速的血流成像,优于传统的代技术.

    更多相关视频

    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
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    相关实验视频

    Last Updated: Sep 11, 2025

    Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
    12:24

    Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

    Published on: July 17, 2012

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    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
    13:43

    Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

    Published on: June 24, 2013

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    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

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    结论:

    • DCT-UNet网络代表了基于DCT的血流成像技术的重大进步.
    • 这种深度学习框架克服了传统方法的关键局限性,提供了更好的性能.
    • 在生理学研究和临床环境中,DCT-UNet显示出未来应用的巨大潜力.