在临床MRI系统上再现白质扩散标准模型
Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems
摘要：
估算轴索内和轴索外微结构参数，如体积分数和扩散率，一直是MRI脑微结构成像的主要努力之一。白质扩散的标准模型（SM）统一了基于嵌入局部各向异性轴突外空间的不可渗透窄圆柱体的各种建模方法。然而，估计SM参数从一组传统的扩散MRI（dMRI）测量是病态的。多维dMRI有助于解决估计退化，但仍然需要临床可行的采集，以产生稳健的参数图。在这里，我们通过最小化两个3T扫描仪的基于机器学习的SM参数估计值的均方误差来找到最佳多维协议，其中相应的梯度强度为$40$和$80\,\unit{mT/m}$。我们通过在两种扫描仪上扫描20名健康志愿者两次来评估15分钟最佳方案的扫描仪内和扫描仪间可重复性。除游离水含量外，所有SM参数的变异系数分别为体素方向的$\lesssim 10\%$和区域平均值的$1-4 \%$。由于所实现的SM再现性结果与常规扩散张量成像的结果相似，因此我们的结果能够在神经科学研究和临床中对白质微结构进行稳健的体内映射。
英文摘要：
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of $40$ and $80\,\unit{mT/m}$. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are $\lesssim 10\%$ voxelwise and $1-4 \%$ for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
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