A STAP algorithm approach to fMRI: a simulation study

Partially adaptive STAP algorithm approaches to functional MRI

In this paper, the architectures of three partially adaptive space-time adaptive processing (STAP) algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in the construction of brain activation maps in functional magnetic resonance imaging (fMRI). Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis.

A parallel approach to STAP implementation for fMRI data

PURPOSE

To exploit the capabilities of parallel processing in applying the space-time adaptive processing (STAP) algorithm, previously explored on a small scale for functional magnetic resonance imaging (fMRI) applications, to conventional size fMRI data sets.

MATERIALS AND METHODS

STAP is a two-dimensional filter that is able to locate fMRI activations in both space and frequency. It is applied here for the construction of brain activation maps in fMRI using Visual Age C, incorporating Engineering and Scientific Subroutine Library (ESSL) functions, compiled in 64-bit, and executed on an IBM SP supercomputer.

RESULTS

Computer simulations incorporating actual MRI noise indicate that STAP, incorporated using the method of steepest descent, is feasible on conventional size data sets and exhibits an improvement in detecting activations over the more traditional cross correlation method of fMRI analysis when the response is unknown.

CONCLUSION

STAP is feasible on traditional size fMRI data sets and useful in elucidating spatial and temporal connectivity.