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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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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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基于深度学习的DSCMRI参数图的生成,使用DCEMRI数据.

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这项研究开发了一种深度学习方法,从动态对比增强 (DCE) 的MRI数据中创建动态敏感度对比 (DSC) 的MRI perfusion map,从而减少对比剂量两次的需要.

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 神经成像是一种神经成像.

背景情况:

  • 动态敏感度对比 (DSC) MRI和动态对比增强 (DCE) MRI为临床诊断和研究提供了有价值的 perfusion 参数.
  • 当前的协议通常需要两个加多对比剂量,当使用DSC和DCEMRI在同一会话.
  • 从DCEMRI数据中获得DSC衍生地图的开发方法可以简化成像过程并减少对比剂的使用.

研究的目的:

  • 开发和验证基于深度学习的方法,用于从DCEMRI数据中合成DSC衍生参数图.
  • 为了使得使用单一的对比剂剂量能够获得DSC和DCEMRI参数图.

主要方法:

  • 一个有条件的生成对抗网络 (cGAN) 用64名参与者 (包括脑瘤患者) 的数据集被设计和训练.
  • 参考DSCMRI参数图是在DCEMRI之后获得的.
  • 通过比较合成DSC地图与使用线性回归和布兰德-阿尔特曼分析的地面真实DSC地图来评估cGAN的性能.

主要成果:

  • cGAN成功地从DCEMRI数据中合成了现实的DSC参数图.
  • 合成的参数显示了健康对照组中与基准真实值相似的分布.
  • 在脑瘤患者中,瘤区域的合成参数与基本真相值有强烈的线性相关性.
  • 从DCE衍生出的DSC地图可视化了在传统的DSCMRI中被敏感性工件所掩盖的区域.

结论:

  • 深度学习使得从DCEMRI数据中合成DSC衍生参数图,即使在易受人工物影响的区域.
  • 这种方法具有很大的潜力,可以通过单次注射对比剂获得全面的 perfusion 信息 (DSC 和 DCE).
  • 这些发现表明,对先进的神经成像进行更有效,更为友好的治疗方法.