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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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基于物理学的神经网络用于血流模型中的参数估计.

Jeremías Garay1, Jocelyn Dunstan2, Sergio Uribe3

  • 1Department of Mechanical and Metallurgical Engineering, Pontificia Universidad Católica de Chile, Chile; Center of Biomedical Imaging, Pontificia Universidad Católica de Chile, Chile; Millennium Institute for Intelligent Healthcare Engineering (iHealth), Chile.

Computers in biology and medicine
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概括

基于物理学的神经网络 (PINNs) 从有限的血液动力学数据有效地估计参数和速度场. 这种深度学习方法对复杂的物理系统模拟非常有希望,其性能优于具有更多参数的传统方法.

关键词:
血液的流动 血液的流动血液动力学 血液动力学患者特定的模型基于物理学的神经网络.减少的订单建模减少的订单建模

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科学领域:

  • 计算流体动力学的流体动力学.
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 基于物理学的神经网络 (PINNs) 对具有不完整数据的反向问题非常有价值.
  • 血液动力学由于难以进行边界建模和稀缺的高质量测量提出了挑战.

研究的目的:

  • 应用PINNs来估计大动脉中减少顺序模型参数和速度场.
  • 通过噪音散射测量,分析静态和瞬态流量模式中的性能.

主要方法:

  • 利用PINNs的方法来估计参数和重建速度场.
  • 研究了两个不同的流动模式:静态和瞬态.
  • 将PINNs的性能与卡尔曼波器方法进行比较.

主要成果:

  • 从模拟数据中获得了强大而准确的参数估计.
  • 速度重建的准确性取决于测量质量和流的复杂性.
  • 在估计更高数量的参数时,PINNs的表现优于卡尔曼过器.

结论:

  • PINNs提供了一种强大的深度学习驱动的方法来模拟复杂的合物理系统.
  • 该方法证明了在推进血液动力学建模和分析方面具有重大潜力.