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.