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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

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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快速MRI成像的知识驱动深度学习:从监督到无监督学习的低样本MR图像重建.

Shanshan Wang1, Ruoyou Wu1, Sen Jia1

  • 1Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Magnetic resonance in medicine
|April 16, 2024
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概括
此摘要是机器生成的。

深度学习 (DL) 通过使用神经网络进行图像重建来加速磁共振成像 (MRI). 本综述探讨了将DL与MRI物理结合在一起的挑战和解决方案,从而推进可靠的成像系统.

关键词:
MR重建的重建 MR重建的重建深度学习是一种深度学习.快速的MRI成像技术

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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
  • 磁共振成像 (MRI) 是一种磁共振成像技术.

背景情况:

  • 深度学习 (DL) 是加速MRI获取和重建的强大工具.
  • 由于基于物理的过程和数据属性,MRI重建存在独特的挑战,与自然图像恢复不同.
  • 整合领域知识与数据驱动的DL方法对于准确的MRI至关重要.

研究的目的:

  • 审查快速MRI的知识驱动DL的重大挑战.
  • 介绍基于DL的MRI重建的显著解决方案和趋势.
  • 讨论MR供应商的采用,开放问题,以及对可靠的MRI系统在DL的未来方向.

主要方法:

  • 对MRI中DL应用的现有文献的审查.
  • 分析基于知识的DL方法,包括神经网络架构.
  • 学习范式的检查:MRI重建中的监督,半监督和无监督学习.

主要成果:

  • 在将DL应用于MRI的基于物理的性质方面发现了关键挑战.
  • 突出了域知识与DL模型的成功整合.
  • 记录了学习策略从监督到无监督方法的演变.

结论:

  • 知识驱动的DL为加速MRI提供了显著的潜力.
  • 解决特定的MRI挑战需要定制的DL解决方案和学习策略.
  • 未来的研究应该专注于针对基于DL的强大可靠的MRI系统的开放问题.