The Mind Research Network, Albuquerque, NM 87106, USA.
Neuroimage. 2011 Feb 14;54(4):2867-84. doi: 10.1016/j.neuroimage.2010.10.063. Epub 2010 Oct 26.
We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D denoising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional denoising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the denoised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of denoised wavelet coefficients for each voxel. Given the de-correlated nature of these denoised wavelet coefficients, it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules: First, in the analysis module we combine a new 3-D wavelet denoising approach with signal separation properties of ICA in the wavelet domain. This step helps obtain an activation component that corresponds closely to the true underlying signal, which is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing+spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false positive voxels.
我们提出了一种新颖的基于小波域的整合方法（w-ICA），用于 3-D 去噪功能磁共振成像（fMRI）数据，然后在小波域中使用独立成分分析（ICA）进行源分离分析。我们提出了一种基于 3-D 小波的多方向去噪方案的想法，其中 4-D fMRI 数据集的每个体积都使用轴、矢状和冠状几何结构进行子采样，以获得同一数据的三个不同的逐片表示。任意体素的滤波强度值是通过对每个子带的三个观察几何结构对应的去噪小波系数计算得到的。这导致了每个体素的一组稳健的去噪小波系数。由于这些去噪小波系数的去相关性质，可以使用小波域中的 ICA 获得更准确的源估计。这项工作的贡献可以体现在两个模块中：首先，在分析模块中，我们将一种新的 3-D 小波去噪方法与小波域中的 ICA 的信号分离特性结合起来。这一步有助于获得与真实潜在信号密切对应的激活分量，该分量相对于其他分量是最大独立的。其次，我们提出并描述了两种新的形状指标，用于通过不同框架获得的激活区域之间的后-ICA 比较。我们使用模拟和真实 fMRI 数据验证了我们的方法，并将结果与传统方案（高斯平滑+空间 ICA：s-ICA）进行了比较。结果表明，基于两个重要特征，我们的方法有显著的改进：（1）保持激活区域的形状（形状指标）和（2）接收者操作特性曲线。观察到，即使在非常高的噪声水平下，所提出的框架也能够以一致的方式保留实际的激活形状，并且还显著减少了假阳性体素。
