Signal subspace based enhancement and MAP parameter estimation of fMRI signals

Abstract

We propose a signal subspace based functional magnetic resonance image (fMRI) signal enhancement followed by maximum a posteriori (MAP) estimation of the parameters of a hemodynamic response function (HRF). The fMRI time-series, which is corrupted by physiological and scanner noise, is a low SNR signal. This signal is projected onto signal-plus-noise space and then enhanced in this space. The enhanced signal is then used to estimate the parameters of the HRF using MAP estimation. Preliminary results indicate that signal enhancement greatly improves the estimation performance