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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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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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在深度学习辅助的基于模型的非卡特西亚SWI非卡特西亚SWI外共振校正.

Guillaume Daval-Frérot1,2,3, Aurélien Massire1, Boris Mailhé4

  • 1Siemens Healthineers, Saint-Denis, France.

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概括

一种结合基于模型和深度学习方法的新型混合方法显著加快了加速MRI扫描的非共振校正. 这项创新加快了图像重建的速度,使先进的技术更适合临床使用.

关键词:
3D闪闪发光 3D闪闪发光深度学习是一种深度学习.非卡特西安成像技术的成像异共振校正 异共振校正一个未卷起的神经网络.

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科学领域:

  • 磁共振成像 (MRI) 是一种磁共振成像.
  • 医疗图像重建 医疗图像重建
  • 医学成像中的深度学习

背景情况:

  • 患者诱导的磁场不均质性会在MRI中引起人工物 (非共振),特别是在像SWI这样的长读出序列中.
  • 传统的校正方法对于在临床环境中进行高分辨率,加速的3D非卡特西安多线圈采集来说太慢了.

研究的目的:

  • 开发和评估一条混合管道,将基于模型和神经网络的方法结合起来,用于MRI中的加速非共振校正.
  • 为了减少在苛刻的MRI采集中对外共振校正的计算时间.

主要方法:

  • 使用UNets的混合管道接受了加速3D SWI SPARKLING采集的培训.
  • 使用基于模型的方法和深度学习的组合进行了非共振校正.
  • 性能与仅模型和仅网络管道进行了比较,使用缓慢的,基于模型的校正作为基本事实.

主要成果:

  • 拟议的混合管道实现了重建速度比基线方法快两到三倍.
  • 神经网络起到了预先条件的作用,并提供了代间的记忆,增强了模型设计的灵活性.
  • 观察到加速因子和模型/网络组件之间的协同作用.

结论:

  • 基于模型和基于网络的非共振校正的组合有效地加速了传统方法.
  • 混合方法通过显著减少重建时间,显示出临床应用的前景.
  • 进一步探索模型/网络协同作用可能会导致加速MRI的未来进展.