Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invariant spherical convolutional neural networks can improve microstructural parameter estimation. We trained a spherical convolutional neural network to predict the ground-truth parameter values from efficiently simulated noisy data and applied the trained network to imaging data acquired in a clinical setting to generate microstructural parameter maps. Our network performed better than the spherical mean technique and multi-layer perceptron, achieving higher prediction accuracy than the spherical mean technique with less rotational variance than the multi-layer perceptron. Although we focused on a constrained two-compartment model of neuronal tissue, the network and training pipeline are generalizable and can be used to estimate the parameters of any Gaussian compartment model. To highlight this, we also trained the network to predict the parameters of a three-compartment model that enables the estimation of apparent neural soma density using tensor-valued diffusion encoding.

翻译：扩散磁共振成像对脑组织的微观结构特性敏感。然而，从测量信号中估计具有临床和科学意义的微观结构参数仍是一个极具挑战性的逆问题，而机器学习可能有助于解决这一难题。本研究探讨了近期发展的旋转不变球面卷积神经网络能否改进微观结构参数估计。我们训练了一个球面卷积神经网络，用于从高效模拟的含噪声数据中预测真实参数值，并将训练好的网络应用于临床环境下采集的成像数据以生成微观结构参数图。与球面均值技术及多层感知机相比，我们的网络表现更优，实现了比球面均值技术更高的预测精度，同时比多层感知机具有更低的旋转方差。尽管本研究聚焦于神经元组织的约束双室模型，但该网络与训练流程具有普适性，可扩展至任意高斯室模型的参数估计。为突出这一特性，我们还训练网络预测三室模型参数，该模型可利用张量值扩散编码实现表观神经元胞体密度的估计。