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相关概念视频

Computed Tomography01:10

Computed Tomography

8.0K
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...
8.0K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.8K
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...
2.8K
Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

1.5K
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
1.5K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

280
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...
280

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相关实验视频

Updated: Jan 13, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
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Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

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通过扩散概率模型加速直接材料分解,用于Sparse-view光谱计算机断层扫描.

Jie Guo1, Ailong Cai1, Junru Ren1

  • 1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Information Engineering University, Zhengzhou, China.

Journal of X-ray science and technology
|October 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种无监督的深度学习方法,用于光谱计算机断层扫描 (光谱CT) 材料分解. 这种新的方法提高了图像质量和准确性,即使数据稀疏和几何不一致.

关键词:
扩散的概率模型.材料的分解材料的分解频谱CT CT 测试结果虚拟单色图像的虚拟单色图像

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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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相关实验视频

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Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
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Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

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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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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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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
  • 人工智能在医学中的应用

背景情况:

  • 精确的材料分解对于光谱CT应用至关重要.
  • 传统的方法与稀疏视图工件,缓慢的融合,噪音和不良位置,特别是在几何不一致的成像中扎.

研究的目的:

  • 开发一个无监督的深度学习框架,用于在稀疏视野光谱CT中直接进行材料分解.
  • 克服使用虚拟单色图像 (VMIs) 和概率扩散模型的传统基于模型的方法的局限性.

主要方法:

  • 提出了一个无监督的深度学习框架,通过材料分解的概率扩散模型优化VMIs.
  • 集成的VMIs以增强多色投影的差异化,并解决代重建的融合问题.
  • 采用双重约束:用于测量一致性的数据忠实性和用于解剖学可信性的概率规范化.

主要成果:

  • 对软组织重建的峰值信号噪声比率 (PSNR) 提高了10dB,结构相似性 (SSIM) 增加了4.31%,相比90投影的现有方法.
  • 在稀疏采样和几何不一致的情况下,证明了强度并保持了重建保真性.
  • 根据临床前数据验证的有效性.

结论:

  • 拟议的深度学习框架显著改善了稀疏视野光谱CT中的材料分解.
  • 该方法提供了更高的准确性和稳定性,克服了当前技术的关键挑战.
  • 这种方法对推进需要高准确度材料分解的光谱CT应用有前途.