fMRI Signal Restoration Using a Spatio-Temporal Markov Random Field Preserving Transitions

In fMRI studies, Gaussian filtering is usually applied to improve the detection of activated areas. Such lowpass filtering enhances the signal to noise ratio. However, undesirable secondary effects are a bias on the signal shape and a blurring in the spatial domain. Neighboring activated areas may be merged and the high resolution of the fMRI data compromised. In the temporal domain, activation and deactivation slopes are also blurred. We propose an alternative to Gaussian filtering by restoring the signal using a spatiotemporal Markov Random Field which preserves the shape of the transitions. We define some interaction between neighboring voxels which allows us to reduce the noise while preserving the signal characteristics. An energy function is defined as the sum of the interaction potentials and is minimized using a simulated annealing algorithm. The shape of the hemodynamic response is preserved leading to a better characterization of its properties. We demonstrate the use of this approach by applying it to simulated data and to data obtained from a typical fMRI study.