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An Improved Space-Time Adaptive Processing (STAP) Model: A Spatiotemporal Approach For fMRI

An Improved Space-Time Adaptive Processing (STAP) Model: A Spatiotemporal Approach For fMRI. Lejian Huang 1 , Elizabeth A. Thompson 2 , Mary Comer 1 , Thomas M. Talavage 1 , Scott K. Holland 3 , Vincent Schmithorst 3 1 School of Electrical and Computer Engineering,

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An Improved Space-Time Adaptive Processing (STAP) Model: A Spatiotemporal Approach For fMRI

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  1. An Improved Space-Time Adaptive Processing (STAP) Model: A Spatiotemporal Approach For fMRI Lejian Huang1, Elizabeth A. Thompson2, Mary Comer1, Thomas M. Talavage1, Scott K. Holland3, Vincent Schmithorst3 1School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 2Department of Engineering, Purdue University, Fort Wayne, IN 3Imaging Research Center, Children’s Hospital Medical Center, Cincinnati, OH

  2. Outline • fMRI Data Analysis Methods and STAP • The New Model for STAP • Results • Conclusions

  3. fMRI Data Analysis Methods • Univariate methods • Cross-correlation • General Linear Model • Spatiotemporal methods • Principal Component Analysis (PCA) • Independent Component Analysis (ICA) • Space-Time Adaptive Processing (STAP)

  4. STAP: Radar to fMRI • Input: • Radar: time-series from 2D array of antennae • fMRI: time-series from 2D array of pixels • Output: coefficient of the targeted, periodic 2D signal • Radar: underlying periodic image of target • fMRI: underlying periodic image of activation

  5. Advantage of STAP • STAP is a two-dimensional filter that simultaneously determines spatial location as well as frequency content of a given activation.

  6. noise noisy signal Introduce in next slide STAP Overview Reshape 3D noise data to a column vector Generate noise covariance matrix Compute weight matrix similar to Weiner filter Determine steering matrix t t Reshape 3D noisy signal to a column vector Generate coefficient of targeted, periodic 2D signal

  7. [ 1; ej2πω; ej2πω·2; …ej2πω·(N-1)] 1 … N TR Steering matrix • A Kronecker product (an operation on two matrices of arbitrary size resulting in a block matrix ) of temporal steering matrix and spatial steering matrix • Temporal steering matrix is the DFT of frequency resolution • Spatial steering matrix represents the extent of correlation between each active pixel to its neighboring New model will change this spatial steering matrix

  8. Outline • fMRI Data Analysis Methods and STAP • The New Model for STAP • Results • Conclusions

  9. STAP in fMRI • Current implementation assumes spatial independence of voxels active voxel

  10. Independence ≠ Truth • Point spread function (PSF)* • Non-punctate • Fits to a Gaussian shape 3.5 mm *C.A. Olman et al., “Point Spread Function for Gradient Echo and Spin Echo BOLD fMRI at 7 Tesla,” Proceedings of 14th ISMRM, 1066

  11. PSF Model for STAP • Point spread function weighting matrix f(x,y) = exp[-(x2+y2) / 0.2739] active voxel

  12. Incorporation of PSF in STAP • Circular convolution of PSF (as window) with previous spatial steering matrix One column of original STAP spatial steering matrix: Convolve with PSF: Obtain one column of new STAP spatial steering matrix:

  13. Outline • fMRI Data Analysis Methods and STAP • The New Model for STAP • Results • Conclusions

  14. Synthetic Data Analysis • Simulated activations were added to each time course to simulate a fingertapping exercise of frequency 0.0167 Hz. Amplitude was 4% relative to the mean intensity value for the time course of the given voxel. Representative time course (TR=3s)

  15. New Model Improves STAP

  16. Cross-correlation (gold standard) STAP: Old Model STAP: New Model Human Brain Data Result 30S 30S Rest ON OFF ON OFF OFF ON Finger tapping

  17. Outline • fMRI Data Analysis Methods and STAP • The New Model for STAP • Results • Conclusions

  18. Conclusions • Synthetic data • Introduction of PSF has improved STAP performance • Human brain data • Results consistent with "gold standard" • Fewer isolated "active" voxels after inclusion of PSF • Still need effective statistical model for p-values • Inclusion of PSF makes STAP more useful

  19. Acknowledgements • NIH grant 1R21MH68267-01A1 • Purdue Research Foundation

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