Improved 3D wavelet-based de-noising of fMRI data

Siddharth Khullar, Andrew M. Michael, Nicolle Correa, Tulay Adali, Stefi A. Baum, Vince Daniel Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7962
DOIs
StatePublished - 2011
Externally publishedYes
EventMedical Imaging 2011: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 14 2011Feb 16 2011

Other

OtherMedical Imaging 2011: Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/14/112/16/11

Fingerprint

Magnetic Resonance Imaging
Wavelet Analysis
Independent component analysis
smoothing
wavelet analysis
Wavelet transforms
Noise
modules
Inverse transforms
shrinkage
preserving
Chemical activation
activation
coefficients
estimates

Keywords

  • fMRI
  • ICA
  • image denoising
  • wavelets

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Khullar, S., Michael, A. M., Correa, N., Adali, T., Baum, S. A., & Calhoun, V. D. (2011). Improved 3D wavelet-based de-noising of fMRI data. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 7962). [79624P] https://doi.org/10.1117/12.878276

Improved 3D wavelet-based de-noising of fMRI data. / Khullar, Siddharth; Michael, Andrew M.; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince Daniel.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7962 2011. 79624P.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khullar, S, Michael, AM, Correa, N, Adali, T, Baum, SA & Calhoun, VD 2011, Improved 3D wavelet-based de-noising of fMRI data. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 7962, 79624P, Medical Imaging 2011: Image Processing, Lake Buena Vista, FL, United States, 2/14/11. https://doi.org/10.1117/12.878276
Khullar S, Michael AM, Correa N, Adali T, Baum SA, Calhoun VD. Improved 3D wavelet-based de-noising of fMRI data. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7962. 2011. 79624P https://doi.org/10.1117/12.878276
Khullar, Siddharth ; Michael, Andrew M. ; Correa, Nicolle ; Adali, Tulay ; Baum, Stefi A. ; Calhoun, Vince Daniel. / Improved 3D wavelet-based de-noising of fMRI data. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 7962 2011.
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