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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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相关实验视频

Updated: Jul 9, 2025

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
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Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

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一个基于深度学习的MRI反假名自我超分辨率算法.

Can Zhao1, Aaron Carass1,2, Blake E Dewey1

  • 1Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于磁共振 (MR) 成像的反别名和自我超分辨率 (AA-SSR) 算法. 这种新的方法提高了图像分辨率,而不需要外部训练数据,克服了当前技术的局限性.

关键词:
在美国,CNN是CNN.这就是为什么MRI是MRI.别名: aliasing 别名: 抛售 抛售 抛售深度网络是一个深度网络.自动超级分辨率自动超级分辨率

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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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
  • 图像处理 图像处理

背景情况:

  • 高分辨率磁共振 (MR) 成像对于临床应用至关重要,但面临的挑战是采集时间,信号噪声比和运动工件.
  • 传统的二维MRI成像协议往往会损害透平面分辨率,引入抗标准插值的别名化工件.
  • 现有的超分辨率 (SR) 方法通常需要配对的低分辨率 (LR) 和高分辨率 (HR) 训练数据,由于扫描仪的限制,这些数据通常无法获得.

研究的目的:

  • 为MR图像开发一个反别名 (AA) 和自我超分辨率 (SSR) 算法,消除了对外部训练数据的需求.
  • 为提高分辨率,利用机内磁共振图像片中的高频信息来提高分辨率.
  • 为了提高空间分辨率和减少MR图像中的别名工件,而不依赖于配对的训练数据集.

主要方法:

  • 一个新的算法结合了自我反称 (SAA) 深度网络,其次是自我超分辨率 (SSR) 深度网络.
  • 在原始MR图像中沿多个方向应用SAA和SSR网络.
  • 使用福里埃爆积累重新组合定向特定输出,用于最终图像重建.

主要成果:

  • 拟议的SAA+SSR算法显示了MR图像质量的显著改善.
  • 该方法有效地减少了别名化工件,并增强了空间分辨率.
  • 在没有大量预处理的情况下,在各种MR数据上验证了性能,显示出对现有SSR技术的优越性.

结论:

  • 开发的AA-SSR算法提供了一个可行的解决方案,可以在没有外部训练数据的情况下提高MR图像分辨率.
  • 这种方法有效地解决了临床环境中传统的插值和基于学习的SR方法的局限性.
  • 该算法显示,它有望在各种应用中提高MRI成像的质量和诊断实用性.