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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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Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain
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培训基于深度学习的动态MR图像重建,使用开源自然视频.

Olivier Jaubert1, Michele Pascale1, Javier Montalt-Tordera1

  • 1UCL Centre for Translational Cardiovascular Imaging, University College London, 30 Guilford St, London, WC1N 1EH, UK.

Scientific reports
|May 23, 2024
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概括

深度学习 (DL) 模型可以使用自然视频重建动态MR图像,克服数据限制. 虽然心脏数据产生了更好的模拟,但这两种训练方法在实时扫描中表现相似,性能优于压缩传感.

关键词:
深度学习是一种深度学习.动态核磁共振 (MRI) 是一种动态核磁共振.图像重建 图像重建机器学习 机器学习实时实时的时间.

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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
  • 生物物理学的生物物理.

背景情况:

  • 动态MRI成像需要高效的重建技术.
  • 数据稀缺和共享局限性阻碍了深度学习模型的开发.
  • 自然视频为培训提供了潜在的替代数据来源.

研究的目的:

  • 开发和评估用于使用自然视频进行动态MR图像重建的深度学习管道.
  • 使用不同的DL架构和采样模式来比较重建性能.
  • 评估训练DL模型与来自自然视频的模拟MR数据的可行性.

主要方法:

  • 训练有素的深度学习网络 (VarNet,3D UNet,FastDVDNet) 使用心脏和自然视频衍生的合成MR数据.
  • 使用训练有素的DL网络和压缩传感器重建实时低采样动态MR图像.
  • 使用模拟指标 (MSE,PSNR,SSIM) 和前性评估 (图像质量排名,SNR,边缘清晰度) 评估性能.

主要成果:

  • 用心脏数据训练的深度学习模型在模拟中表现优于用自然视频训练的模型.
  • 这两种DL方法在模拟中显著优于压缩传感 (p < 0.05).
  • 展望评估显示,与压缩传感相比,DL重建的排名相似,优越,SNR或边缘度没有显著差异.

结论:

  • 一个DL管道可以从自然视频中学习动态MR重建,保持高质量,快速的重建.
  • 这种方法减轻了与数据稀缺和共享MR成像相关的局限性.
  • 开发的数据集,代码和网络是公开可用的,以推进研究.