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相关概念视频

Magnetic Resonance Imaging01:24

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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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一个为多个:以物理为基础的合成数据增强了可概括的深度学习,用于快速的MRI重建.

Zi Wang1, Xiaotong Yu2, Chengyan Wang3

  • 1Department of Electronic Science, Xiamen University-Neusoft Medical Magnetic Resonance Imaging Joint Research and Development Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, China; Department of Bioengineering and Imperial-X, Imperial College London, United Kingdom.

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PubMed
概括

一个新的框架 - - 物理信息合成数据学习框架 (PISF) - - 能够实现通用深度学习,用于快速使用合成数据进行磁共振成像 (MRI) 重建. 这种方法显著减少了对现实世界的数据的依赖,提高了可访问性.

关键词:
深度学习是一种深度学习.图像重建 图像重建磁共振成像技术 磁共振成像技术综合数据 综合数据

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 磁共振成像 (MRI) 提供无辐射的解剖洞察力,但由于扫描时间长.
  • K空间低样本加快了MRI,但引入了需要复杂重建的文物.
  • 目前用于快速MRI重建的深度学习 (DL) 方法面临着数据采集成本,隐私和在各种场景中概括的挑战.

研究的目的:

  • 引入一个新的框架,物理信息合成数据学习框架 (PISF),用于可概括和快速的MRI重建.
  • 通过减少对大型真实世界MRI数据集的依赖来克服现有的DL方法的局限性.
  • 为了使单个DL模型能够在多个成像参数和中心进行高质量的重建.

主要方法:

  • 开发了一个物理信息合成数据学习框架 (PISF),用于快速MRI重建.
  • 将分解的2D图像重建成多个1D问题,从1D合成数据合成开始,以提高概括性.
  • 采用增强学习技术来训练合成数据上的DL模型.

主要成果:

  • 在体内实现的MRI重建与在真实数据上训练的模型相比或优于真实数据,减少了高达96%的真实数据需求.
  • 通过单个PISF模型,在4个采样模式,5个解剖学,6个对比,5个供应商和7个中心中表现出了显著的概括性.
  • 通过专家评估验证了对神经和心血管患者群体的适应性.

结论:

  • PISF为快速MRI重建提供了可行且具有成本效益的解决方案.
  • 该框架显著提高了深度学习在各种MRI应用中的通用性.
  • PISF促进了加速MRI技术的更广泛采用,改善了诊断的可访问性.