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Proceedings Paper

Improved 3D wavelet-based de-noising of fMRI data
Author(s): Siddharth Khullar; Andrew M. Michael; Nicolle Correa; Tulay Adali; Stefi A. Baum; Vince D. Calhoun
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Paper Abstract

Functional MRI (fMRI) data analysis deals with the problem of detecting very weak signals in very noisy data. Smoothing with a Gaussian kernel is often used to decrease noise at the cost of losing spatial specificity. We present a novel wavelet-based 3-D technique to remove noise in fMRI data while preserving the spatial features in the component maps obtained through group independent component analysis (ICA). Each volume is decomposed into eight volumetric sub-bands using a separable 3-D stationary wavelet transform. Each of the detail sub-bands are then treated through the main denoising module. This module facilitates computation of shrinkage factors through a hierarchical framework. It utilizes information iteratively from the sub-band at next higher level to estimate denoised coefficients at the current level. These de-noised sub-bands are then reconstructed back to the spatial domain using an inverse wavelet transform. Finally, the denoised group fMRI data is analyzed using ICA where the data is decomposed in to clusters of functionally correlated voxels (spatial maps) as indicators of task-related neural activity. The proposed method enables the preservation of shape of the actual activation regions associated with the BOLD activity. In addition it is able to achieve high specificity as compared to the conventionally used FWHM (full width half maximum) Gaussian kernels for smoothing fMRI data.

Paper Details

Date Published: 15 March 2011
PDF: 9 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79624P (15 March 2011);
Show Author Affiliations
Siddharth Khullar, Chester F. Carlson Ctr. for Imaging Science, Rochester Institute of Technology (United States)
The Mind Research Network (United States)
Andrew M. Michael, The Mind Research Network (United States)
Nicolle Correa, Univ. of Maryland (United States)
Tulay Adali, Univ. of Maryland (United States)
Stefi A. Baum, Chester F. Carlson Ctr. for Imaging Science, Rochester Institute of Technology (United States)
Vince D. Calhoun, The Mind Research Network (United States)
Chester F. Carlson Ctr. for Imaging Science, Rochester Institute of Technology (United States)
Univ. of New Mexico (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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