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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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X-ray Imaging01:24

X-ray Imaging

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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 1, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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在复杂的媒体中进行成像的相关联信息有序字典学习.

Miguel Moscoso1, Alexei Novikov2, George Papanicolaou3

  • 1Department of Mathematics, Universidad Carlos III de Madrid, Leganes, Madrid 28911, Spain.

Proceedings of the National Academy of Sciences of the United States of America
|March 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的两步成像方法,用于使用词典学习和多维缩放来分散媒体. 该技术成功地重建了与同质媒体相比较的分辨率的图像,即使是未知来源信息.

关键词:
复杂的媒体.词典学习学习词典学习影像成像技术 影像成像技术多维缩放的多维缩放.

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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科学领域:

  • 波浪物理学的波浪物理.
  • 计算机成像成像技术
  • 信号处理 信号处理

背景情况:

  • 由于信号扭曲,散射介质中的成像具有挑战性.
  • 现有的方法往往需要先前了解源位置或属性.
  • 大量和多样化的数据集对于先进的成像技术至关重要.

研究的目的:

  • 开发一种针对散射介质的强大的成像方法.
  • 为了使图像重建在没有对源参数的预先了解的情况下.
  • 在复杂的环境中实现高分辨率成像.

主要方法:

  • 这是一个两步的方法,结合了词典学习和多维缩放.
  • 从无序数组数据中使用字典学习估计格林的函数向量.
  • 通过多维缩放和交叉相关性对传感矩阵列进行排序,用于图像重建.

主要成果:

  • 从稀疏的,未知的源数据成功估计了真正的格林函数向量.
  • 有序的传感矩阵列,以实现准确的成像.
  • 在模拟中实现的图像分辨率与同质介质相比较.

结论:

  • 拟议的方法有效地影像复杂的散射介质.
  • 它克服了未知来源信息的局限性.
  • 在具有挑战性的环境中展示了高分辨率成像的潜力.