基于全局概率密度函数的时域非局部均值滤波揭示了脑连接性的个体差异。
GLOBAL PDF-BASED TEMPORAL NON-LOCAL MEANS FILTERING REVEALS INDIVIDUAL DIFFERENCES IN BRAIN CONNECTIVITY.
作者信息
Li Jian, Choi Soyoung, Joshi Anand A, Wisnowski Jessica L, Leahy Richard M
机构信息
Signal and Image Processing Institute, University of Southern California, Los Angeles, 90089.
Neuroscience Graduate Program, University of Southern California, Los Angeles, 90089.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:15-19. doi: 10.1109/ISBI.2018.8363513. Epub 2018 May 24.
Abstract
Characterizing functional brain connectivity using resting fMRI is challenging due to the relatively small BOLD signal contrast and low SNR. Gaussian filtering tends to undermine the individual differences detected by analysis of BOLD signal by smoothing signals across boundaries of different functional areas. Temporal non-local means (tNLM) filtering denoises fMRI data while preserving spatial structures but the kernel and parameters for tNLM filter need to be chosen carefully in order to achieve optimal results. Global PDF-based tNLM filtering (GPDF) is a new, data-dependent optimized kernel function for tNLM filtering which enables us to perform filtering with improved effects adjacent functional regions.
摘要
由于相对较小的血氧水平依赖（BOLD）信号对比度和低信噪比，使用静息态功能磁共振成像（fMRI）来表征大脑功能连接具有挑战性。高斯滤波往往会通过平滑不同功能区域边界的信号来破坏通过BOLD信号分析检测到的个体差异。时域非局部均值（tNLM）滤波在保留空间结构的同时对fMRI数据进行去噪，但为了获得最佳结果，需要仔细选择tNLM滤波器的内核和参数。基于全局概率密度函数（PDF）的tNLM滤波（GPDF）是一种新的、依赖于数据的tNLM滤波优化内核函数，它使我们能够对相邻功能区域进行具有更好效果的滤波。
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