Paper Info
Title
Adaptive denoising of event-related functional magnetic resonance imaging data using spectral subtraction.
Abstract
A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.
Year
DOI
Venue
2004
10.1109/TBME.2004.831525
IEEE transactions on bio-medical engineering
Keywords
Field
DocType
rician statistical model,spectral subtraction,spectral analysis,image denoising,independent component analysis,physiological models,adaptive signal denoising,biomedical mri,event-related functional magnetic resonance imaging,noise-suppressed power spectrum,adaptive signal-preserving technique,principal component analysis,medical image processing
Noise reduction,Computer vision,Parametric model,Computer science,Spectral density,Independent component analysis,Statistical model,Artificial intelligence,Subtraction,Event-related functional magnetic resonance imaging,Rician fading
Journal
Volume
Issue
ISSN
51
11
0018-9294
Citations 
PageRank 
References 
6
0.56
6
Authors
1
Name
Order
Citations
PageRank
Y. M. Kadah119218.80