Noise suppression of FMRI time-series in wavelet domain
نویسندگان
چکیده
Because of poor signal-to-noise ratio (SNR) of the fMRI time series and confounding effects, the results of fMRI analysis are often unsatisfactory. Existence of significant noise and artifacts in fMRI time-series as well as their unknown structure, complicates the problem of activation detection in the time domain. This makes the fMRI noise suppression a challenging problem. Based on some assumptions, different parametric denoising methods such as wavelet based denoising methods have been introduced in the literature. But these assumptions may not necessarily hold for the fMRI data. To remedy this problem, using randomization analysis, we propose a novel wavelet-based denoising method for fMRI analysis. The proposed denoising method is employed to build a feature space for fMRI cluster analysis and its efficiency is shown using simulated and experimental datasets.
منابع مشابه
Wavelet Transform Denoising for fMRI Analysis
Existence of significant noise and artifacts in the fMRI signal complicates the problem of activation detection in the time domain. Because of poor signal-tonoise ratio (SNR) of the fMRI time series and confounding effects, the results of fMRI analysis are often unsatisfactory. In addition, the structure of fMRI noise is not known and still is an open problem. This makes the fMRI noise suppress...
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