Saunders Adam M, Kim Michael E, Schilling Kurt G, Gore John C, Landman Bennett A, Gao Yurui
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Proc SPIE Int Soc Opt Eng. 2025 Feb;13406. doi: 10.1117/12.3047140. Epub 2025 Apr 11.
Blood oxygenation level-dependent (BOLD) signals in white matter in the brain are anisotropically oriented, so that typical isotropic Gaussian spatial smoothing (GSS) of functional magnetic resonance images (fMRI) blurs across anatomical distributions. Abramian et al. developed a graph signal processing approach to smooth fMRI data along white matter fibers using diffusion MRI (diffusion-informed spatial smoothing, DSS). BOLD signals are modulated by the volume and oxygenation of blood carried by the vasculature, so we extend this method to provide vasculature-informed spatial smoothing (VSS). We collected susceptibility-weighted images and applied a Frangi filter to identify the peak vasculature direction in each voxel, alongside co-registered diffusion MRI and resting-state fMRI, weighting the VSS graph by the agreement of the vasculature directions aligned onto the graph's edges. We acquired resting-state fMRI at 7T using a repetition time of 1.5 seconds and 400 time points. Applying the DSS and VSS filters significantly increased the local functional connectivity measured using regional homogeneity (ReHo) compared to GSS ( < 0.01 using a paired -test), but not when comparing DSS and VSS ( = 0.06). Independent component analysis resulted in less noisy components that agree better with labels from a white matter atlas with a significantly higher Dice score from the VSS filter compared to GSS ( < 0.05 using the Mann-Whitney U-test), and the VSS filter and DSS filter performed comparably ( = 0.06). In this pilot analysis, we find that fMRI data smoothed using VSS are comparable to results generated using DSS. The filtering code is available online (https://github.com/MASILab/vss_fmri).
大脑白质中的血氧水平依赖（BOLD）信号呈各向异性取向，因此功能磁共振成像（fMRI）典型的各向同性高斯空间平滑（GSS）会模糊解剖分布。阿布拉米安等人开发了一种图信号处理方法，利用扩散磁共振成像（扩散信息空间平滑，DSS）沿白质纤维对白质fMRI数据进行平滑处理。BOLD信号受脉管系统携带的血液量和氧合作用的调节，因此我们扩展了该方法以提供脉管系统信息空间平滑（VSS）。我们采集了 susceptibility加权图像，并应用Frangi滤波器识别每个体素中的峰值脉管系统方向，同时采集共配准的扩散磁共振成像和静息态fMRI，通过对齐到图边缘的脉管系统方向的一致性对VSS图进行加权。我们使用1.5秒的重复时间和400个时间点在7T下采集静息态fMRI。与GSS相比，应用DSS和VSS滤波器显著提高了使用局部一致性（ReHo）测量的局部功能连接性（配对t检验，P<0.01），但比较DSS和VSS时则不然（P = 0.06）。独立成分分析产生的噪声成分更少，与白质图谱的标签更一致，与GSS相比，VSS滤波器的Dice分数显著更高（曼-惠特尼U检验，P<0.05），并且VSS滤波器和DSS滤波器的表现相当（P = 0.06）。在这项初步分析中，我们发现使用VSS平滑的fMRI数据与使用DSS生成的结果相当。滤波代码可在线获取（https://github.com/MASILab/vss_fmri）。
