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如果每个voxel用不同的扩散协议来测量呢?

Santiago Coelho1, Gregory Lemberskiy1, Ante Zhu2

  • 1Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA.

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概括

扩散MRI (dMRI) 挑战参数估计中的梯度非线性. 新的协议独立参数估计 (PIPE) 方法能够快速,准确地分析复杂的纤维结构,即使使用不同的扫描协议.

关键词:
扩散磁力共振成像 (MRI) 扩散梯度非线性是指梯度的非线性.机器学习是机器学习.微观结构的微观结构球形卷积的球形卷积.

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科学领域:

  • 医疗成像医学成像
  • 神经成像是一种神经成像.
  • 生物物理学的生物物理.

背景情况:

  • 扩散MRI (dMRI) 正在扩展到强度梯度和低场装置,引入梯度非线性.
  • 这些非线性导致扩散梯度的空间变化,扭曲q空间并阻碍准确的参数估计.
  • 目前的方法与异型外和低效的重新培训扎,以适应不同的协议.

研究的目的:

  • 为dMRI开发一个协议独立的参数估计 (PIPE) 方法.
  • 为了应对dMRI分析中的梯度非线性性的挑战.
  • 为了能够快速准确地估计光纤定向分布函数 (fODFs),尽管协议变化.

主要方法:

  • 提出了一种协议独立的参数估计 (PIPE) 方法,适用于任何基于球状卷积的dMRI模型.
  • 推导出一种节的表示,以隔离梯度非线性质的同otropic 和 anisotropic 效应.
  • 应用PIPE在体内人体MRI数据与线性张量编码.

主要成果:

  • PIPE能够在不到3分钟的时间内对整个大脑进行快速参数估计,即使有显著的梯度非线性.
  • 该方法成功评估了纤维响应和fODF参数.
  • 在不同的b值,扩散/回声时间和其他扫描参数中证明了稳定性.

结论:

  • 在任意梯度非线性存在的情况下,PIPE促进了快速参数估计.
  • 消除了对不同协议的dMRI外安排或重新训练估计器的需求.
  • 适用于各种dMRI模型和采用各种扫描参数获取的数据.