Performance assessment of an algorithm for the alignment of fMRI time series

This paper reports on performance assessment of an algorithm developed to align functional Magnetic Resonance Image (fMRI) time series. The algorithm is based on the assumption that the human brain is subject to rigid-body motion and has been devised by pipelining fiducial markers and tensor based registration methodologies. Feature extraction is performed on each fMRI volume to determine tensors of inertia and gradient image of the brain. A head coordinate system is determined on the basis of three fiducial markers found automatically at the head boundary by means of the tensors and is used to compute a point-based rigid matching transformation. Intensity correction is performed with sub-voxel accuracy by trilinear interpolation. Performance of the algorithm was preliminarily assessed by fMR brain images in which controlled motion has been simulated. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations were retrieved automatically and the value of motion parameters compared to those obtained with the Statistical Parametric Mapping (SPM99) and the Automatic Image Registration (AIR 3.08). Results indicate that the algorithm offers sub-voxel accuracy in performing both misalignment and intensity correction of fMRI time series.