Descombes X, Kruggel F, von Cramon D Y
Max Planck Institute of Cognitive Neuroscience, 22-26 Inselstrasse, Leipzig, 04103, Germany.
Neuroimage. 1998 Nov;8(4):340-9. doi: 10.1006/nimg.1998.0372.
In fMRI studies, Gaussian filtering is usually applied to improve the detection of activated areas. Such lowpass filtering enhances the signal to noise ratio. However, undesirable secondary effects are a bias on the signal shape and a blurring in the spatial domain. Neighboring activated areas may be merged and the high resolution of the fMRI data compromised. In the temporal domain, activation and deactivation slopes are also blurred. We propose an alternative to Gaussian filtering by restoring the signal using a spatiotemporal Markov Random Field which preserves the shape of the transitions. We define some interaction between neighboring voxels which allows us to reduce the noise while preserving the signal characteristics. An energy function is defined as the sum of the interaction potentials and is minimized using a simulated annealing algorithm. The shape of the hemodynamic response is preserved leading to a better characterization of its properties. We demonstrate the use of this approach by applying it to simulated data and to data obtained from a typical fMRI study.
在功能磁共振成像（fMRI）研究中，通常应用高斯滤波来改善激活区域的检测。这种低通滤波提高了信噪比。然而，不良的副作用是信号形状的偏差和空间域中的模糊。相邻的激活区域可能会合并，从而损害fMRI数据的高分辨率。在时间域中，激活和去激活斜率也会变得模糊。我们提出了一种替代高斯滤波的方法，即使用时空马尔可夫随机场恢复信号，该方法可以保留转换的形状。我们定义了相邻体素之间的一些相互作用，这使我们能够在保留信号特征的同时降低噪声。能量函数被定义为相互作用势的总和，并使用模拟退火算法将其最小化。血流动力学反应的形状得以保留，从而能够更好地表征其特性。我们通过将这种方法应用于模拟数据和从典型fMRI研究中获得的数据来证明其用途。
