Motion correction for functional magnetic resonance images
This work addresses the distortions in Functional Magnetic Resonance Images (FMRI) caused by subject motion. FMRI is a non-invasive technique which shows great promise in providing researchers and clinicians with neurological information both about healthy subjects and clinical patients by mapping functional activation within the brain using Echo Planar Imaging (EPI). If reliable information is to be obtained from these images, motion correction must be carried out in order to remove or suppress the artefacts arising from subject movement. This work begins by using exploratory data techniques to describe these artefacts so that they can be characterised according to their origin and spatio-temporal manifestation. Based on testing of the accuracy and consistency of existing rigid-body motion correction methods on FMRI data, a new registration algorithm Motion Correction using the FMRIB Linear Image Registration Tool (MCFLIRT) has been developed. It is shown that while MCFLIRT is both more accurate and more robust than previous methods, rigid-body registration schemes in general cannot completely remove the distortions associated with motion and so subsequent analysis of the images may still be inaccurate. Furthermore, it is demonstrated that failure to use a sufficiently detailed model of subject motion in FMRI can in fact lead to degradation of the images through the use of existing motion correction algorithms. Based on these findings, alternative schemes including non-rigid registration and adaptive real-time methods are evaluated. Leading on from this investigation, a framework for Temporally-Integrated Geometric EPI Realignment (TIGER), incorporating both spatial and temporal information about the images, is proposed. An implementation based on this novel modality-specific model is developed and tested against existing rigid-body registration methods. Results show that this new approach is able to achieve significantly more accurate results than previous methods. The quality of correction provided by this new approach brings more subtle artefacts in the data to the fore, suggesting a number of avenues of further research in this area. These are outlined in the final chapter of the thesis.