Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3804-3808. doi: 10.1109/EMBC46164.2021.9629662.
Conventionally, as a preprocessing step, functional MRI (fMRI) data are spatially smoothed before further analysis, be it for activation mapping on task-based fMRI or functional connectivity analysis on resting-state fMRI data. When images are smoothed volumetrically, however, isotropic Gaussian kernels are generally used, which do not adapt to the underlying brain structure. Alternatively, cortical surface smoothing procedures provide the benefit of adapting the smoothing process to the underlying morphology, but require projecting volumetric data on to the surface. In this paper, leveraging principles from graph signal processing, we propose a volumetric spatial smoothing method that takes advantage of the gray-white and pial cortical surfaces, and as such, adapts the filtering process to the underlying morphological details at each point in the cortex.
传统上，作为预处理步骤，在进一步分析之前，对功能磁共振成像 (fMRI) 数据进行空间平滑处理，无论是在基于任务的 fMRI 上进行激活映射，还是在静息态 fMRI 数据上进行功能连接分析。然而，当对图像进行体积平滑处理时，通常使用各向同性高斯核，而这些核并不能适应大脑的结构。相反，皮质表面平滑处理程序提供了将平滑过程适应于基础形态的优势，但需要将体数据投影到表面上。在本文中，我们利用图信号处理的原理，提出了一种体积空间平滑方法，该方法利用灰白质和软脑膜皮质表面，从而使过滤过程适应于皮质中每个点的基础形态细节。
