Joint spatial denoising and active region of interest delineation in functional magnetic resonance imaging

In region of interest (ROI) based functional magnetic resonance imaging (fMRI) group analysis, errors in delineation of an ROI or inclusion of non-active voxels within an ROI can bias the statistical results. Addressing these concerns, this paper presents a new fMRI processing method that simultaneously refines ROI delineation and spatially denoises fMRI activation statistics within the ROI. The underlying assumption is that activation statistics within a small neighborhood are spatially correlated, thereby exhibit similar levels of influence on the overall ROI's response. Based on this assumption, we first identify outlier voxels as those having undue influence on an ROI's feature. Isolated outlier voxels at region boundaries are then removed, thereby refining the ROI delineation. The remaining outlier voxels are de-weighted based on their influence relative to their neighbors to reduce the effects of voxels deemed falsely active in later analysis. The proposed method was tested on real fMRI data collected from 8 healthy subjects performing a bulb-squeezing motor task at various frequencies. Using the proposed method, enhanced capability for detection of frequency-related activation map feature differences (AMFD) was demonstrated when compared to Gaussian spatial smoothing of ROI activation statistics. The validity of the proposed method is suggested by the fact that using one feature for denoising (e.g. spatial variance) results in greater effect size in another feature (e.g. average activation statistics magnitude). Our results demonstrate the importance of accurate ROI delineation in ROI-based fMRI analysis.