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Proceedings Paper

Multivariate approach to functional MRI analysis for brain function study
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Paper Abstract

Functional MRI (fMRI) is a means of analyzing localized brain activity. It is statistically modeled by the multivariate Gaussian probability distribution (in space) and the time series (in time). However, the currently used analysis method takes an univariate approach. That is, the spatial relationships among voxels are ignored. This paper presents a multivariate analysis method. It formulates fMRI activation foci detection as a sensor-array signal processing problem and converts hypotheses tests of the univariate approach to a computer vision approach. It first creates multiple independent, identical sub-images and then uses a covariance matrix to characterize the multivariate Gaussian environment. Not only it utilizes the voxel intensities but also their spatio-temporal relationships. It achieves computer speed superiority over the existing methods. Results obtained by using simulated images, phantom images, and real fMRI data are included. The theoretical and experimental results obtained by using this approach were in good agreement.

Paper Details

Date Published: 20 May 1999
PDF: 9 pages
Proc. SPIE 3660, Medical Imaging 1999: Physiology and Function from Multidimensional Images, (20 May 1999); doi: 10.1117/12.349585
Show Author Affiliations
Tianhu Lei, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 3660:
Medical Imaging 1999: Physiology and Function from Multidimensional Images
Chin-Tu Chen; Anne V. Clough, Editor(s)

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