Filtering artifact from fMRI data using the stockwell transform

Details Filtering artifact from fMRI data using the stockwell transform Filtering artifact from fMRI data using the stockwell transform

2007-07-17

2007-07-17

Patel, Kanjibhai (Department: 2624)

Image analysis

Image transformation or preprocessing

Fourier transform

C324S307000, C600S410000

Reexamination Certificate

active

10430204

ABSTRACT:
The present invention relates to a method for filtering time-varying MR signal data prior to image reconstruction. A one-dimensional FT is applied to the time-varying MR signal data along each frequency-encode line of k space. The phase p of each complex pair (R, I) of the FT transformed data is calculated to create a phase profile for each frequency-encode line. This process is repeated for all time points of the time-varying MR signal data. The time course of each point within the phase profile is then transformed into Stockwell domain producing ST spectra. Frequency component magnitudes indicative of an artifact are determined and replaced with a predetermined frequency component magnitude. Each of the ST spectra is then collapsed into a one-dimensional function. New real and imaginary values (R′, I′) of the complex Fourier data are calculated based on the collapsed ST spectra which are transformed using one-dimensional inverse Fourier transformation for producing filtered time-varying MR signal data. The method for filtering time-varying MR signal data is highly advantageous by easily identifying high-frequency artifacts within the ST spectrum and filtering only frequency components near the artifacts. Therefore, high-frequency artifacts are substantially removed while the frequency content of the remaining signal is preserved, enabling for example detection of subtle frequency changes occurring over time.

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Mansinha et al., “Local S-spectrum Analysis of 1-D and 2-D Data”, Physics of the Earth and Planetary Interiors, Elsevier Science B.V., No. 103, pp. 329-336, 1997.
Stockwell et al., “Localization of the Complex Spectrum: The S Transform”, IEEE Transactions on Signal Processing, IEEE, vol. 44, No. 4, Apr. 1996.

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