Particle filtering for slice-to-volume motion correction in EPI based functional MRI

Head movement during scanning introduces artificial signal changes and impedes activation detection in fMRI studies. The head motion in fMRI acquired using slice-based Echo Planar Imaging (EPI) sequence can be estimated and compensated by aligning the images onto a reference volume through image registration. Registering EPI images volume by volume fails to consider head motion between slices, leading to biased head motion estimates. Slice-to-volume registration is used to estimate motion parameters for each slice by more accurately representing the image acquisition sequence. However, it is prone to image noise and geometric distortion, resulting in high variance estimates. In this work, we propose a Gaussian particle filter based head motion tracking algorithm to reduce the image misregistration errors. The algorithm models head motion by using a dynamic state space model (SSM) to model continuous slice acquisition thereby providing more accurate motion estimates and voxel position estimates. We demonstrate significant performance improvement of the proposed approach as compared to previous registration-only methods of head motion estimation.

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