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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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Atomic Nuclei: Magnetic Resonance01:05

Atomic Nuclei: Magnetic Resonance

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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
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相关实验视频

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Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
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消灭网络:动态MRI成像的学习消灭关系.

Chentao Cao1,2, Zhuo-Xu Cui3, Qingyong Zhu3

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

Medical physics
|September 4, 2023
PubMed
概括

灭网络 (Annihilation-Net) 是一种可解释的深度学习模型,通过有效地建模框架间关系来增强动态MRI重建. 这种新的方法提高了加速动态磁共振成像 (MRI) 的图像质量.

关键词:
消灭关系的消灭关系.动态磁共振成像技术 动态磁共振成像技术他学会了低级别的学术.

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科学领域:

  • 医疗成像医学成像
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 磁共振成像是一种磁共振成像技术.

背景情况:

  • 深度学习中的低级规范化显示出动态MRI的前景.
  • 现有的方法在捕捉跨框架关系方面缺乏可解释性.

研究的目的:

  • 开发一个可解释的卷积神经网络 (CNN),命名为Annihilation-Net.
  • 为了模拟加速动态MRI的跨框架关系.

主要方法:

  • 利用CNN学习零空间转换来进行低级别的表征,基于汉克尔矩阵-卷积等价.
  • 在一个压缩的传感框架内,集成的低级别与稀疏的约束.
  • 使用HQS代地解决了优化问题,并将未滚动的步骤放入了Annihilation-Net,具有可学习的参数.

主要成果:

  • 消灭网络在心脏电影数据集上表现出卓越的定量和质量性能.
  • 该模型在800张图像上进行了训练,并在118张图像上进行了测试.

结论:

  • 消灭网络在动态MRI重建中提供了更好的解释性.
  • 该方法提高了加速动态MRI的整体重建质量.