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相关概念视频

Computed Tomography01:10

Computed Tomography

4.5K
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...
4.5K
X-ray Imaging01:24

X-ray Imaging

5.5K
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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相关实验视频

Updated: Jul 2, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

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低剂量计算机断层扫描的物理/模型和数据驱动方法:一项调查.

Wenjun Xia1, Hongming Shan2, Ge Wang3

  • 1School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China.

IEEE signal processing magazine
|February 26, 2024
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 在低剂量计算机断层扫描 (LDCT) 中表现有希望,但面临挑战. 基于混合物理和数据驱动的方法为更稳定和可靠的LDCT成像提供了解决方案.

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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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Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
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Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

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相关实验视频

Last Updated: Jul 2, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

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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

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Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
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Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 自2016年以来,深度学习 (DL) 已经显著提升了断层成像,特别是低剂量计算机断层成像 (LDCT).
  • 然而,纯粹的DL方法用于LDCT的消毒和重建面临诸如"黑子"性质和不稳定性等挑战,阻碍了临床应用.

研究的目的:

  • 系统地审查LDCT的物理/基于模型的数据驱动方法.
  • 总结这些混合方法的损失函数和培训策略.
  • 评估性能,并讨论混合LDCT方法的未来方向.

主要方法:

  • 综合成像物理和LDCT模型的混合深度学习模型的审查.
  • 分析这些混合网络中使用的各种损失函数和培训策略.
  • 对LDCT的不同基于物理的DL方法的性能评估.

主要成果:

  • 结合物理/基于模型和数据的元素的混合方法显示出克服LDCT中纯DL的局限性的潜力.
  • 整合物理原理提高了DL模型对LDCT的稳定性和可解释性.
  • 系统审查提供了当前趋势和挑战的全面概述.

结论:

  • 混合物理/基于模型的数据驱动方法代表了推进低剂量CT成像的有希望的方向.
  • 解决不稳定性和可解释性问题对于DL在LDCT的临床翻译至关重要.
  • 需要进一步的研究来优化这些混合方法,并探索未来的方向.