基于小波变换的功能磁共振图像时间序列分步去噪.docxVIP

基于小波变换的功能磁共振图像时间序列分步去噪 　　摘要：　　功能磁共振图像数据中反映大脑神经活动的感兴趣信号常受到结构噪声和随机噪声的影响。为消除上述噪声对分析激活体素的影响，对经过SPM标准预处理的体素时间序列进行Activelets小波变换，并在得到尺度系数及细节系数后，针对两类噪声的不同特点进行分步去噪。第一步，在受结构噪声影响的尺度系数上，选用独立成分分析去识别并消除结构噪声源；第二步，提出一种改进的空域相关去噪算法在细节系数上对信号进行处理。值得注意的是，该算法利用邻域体素之间的相似性，判定所处位置的细节系数反映噪声还是神经活动。实验结果表明，经过这两步处理的数据可有效消除噪声的影响，其中框架位移减少了，尖峰百分比减少了2%，此外由去噪后的信号获得的脑激活图中一些明显的伪激活区得到抑制。　　关键词：　　功能磁共振图像；去噪；结构噪声；随机噪声；小波变换　　中图分类号：　　　　文献标志码：A　　Abstract：　　The neural activity signal of interest is often influenced by structural noise and random noise in functional Magnetic Resonance Imaging data. In order to eliminate noise effects in the analysis of activate voxels， the time series of voxels preprocessed by Statistical Parametric Mapping were transformed by Activelets wavelet. After getting scale coefficient and detail coefficient， the two kinds of noise denoised were eliminated separately according to their corresponding characteristics. Firstly， the Independent Component Analysis was used to identify and eliminate the structural noise sources. Secondly， an improved algorithm for spatial correlation was presented on the detail coefficient. In particular， in the improved algorithm， the voxel similarity in the neighborhood was used to determine whether the detail coefficient reflected the noise or the neural activity. Experimental results show that the processing of data effectively eliminate the effect of noise； specifically， the frame displacement decreased by and the percentage of spikes decreased by 2%； in addition， the false activation regions are obviously restrained in the spatial map got by denoised signals.　　英文关键词Key words：　　functional Magnetic Resonance Imaging ； denoising； structural noise； random noise； wavelet transform　　0引言　　功能磁共振图像兼顾空间及时间分辨率，且具有无入侵性的特点，为研究大脑在一些特定脑区的反应提供了强有力的工具，进而可以帮助研究者更好地理解感觉、认知和运动等信息在人脑中的活动[1]。然而fMRI信号常常受到来源众多的噪声影响，导致大量感兴趣信号的丢失。例如随机噪声不仅会降低信号的信噪比，而且会影响最终血氧依赖水平的敏感性与特异性；而由于机器过热或者头动产生的结构噪声会产生大量的伪激活区[2]。　　传统的数据处理流程包含如高斯滤波、空间平滑等去噪的处理步骤，但是这些去噪方法往往达不到理想的效果[3]。SPM团队