Multimodal MRI Neuroimaging with Motion Compensation Based on Particle Filtering

TL;DR

This paper introduces a particle filter-based method for more accurate head motion estimation in fMRI scans, addressing slice-to-volume registration limitations caused by head movement during imaging.

Contribution

It proposes a novel Gaussian particle filter algorithm that models continuous slice acquisition to improve motion tracking accuracy in fMRI.

Findings

Significant reduction in registration errors compared to traditional methods

Enhanced accuracy in brain activation detection

Improved head motion estimates in fMRI scans

Abstract

Head movement during scanning impedes activation detection in fMRI studies. Head motion in fMRI acquired using slice-based Echo Planar Imaging (EPI) can be estimated and compensated by aligning the images onto a reference volume through image registration. However, registering EPI images volume to volume fails to consider head motion between slices, which may lead to severely biased head motion estimates. Slice-to-volume registration can be used to estimate motion parameters for each slice by more accurately representing the image acquisition sequence. However, accurate slice to volume mapping is dependent on the information content of the slices: middle slices are information rich, while edge slides are information poor and more prone to distortion. In this work, we propose a Gaussian particle filter based head motion tracking algorithm to reduce the image misregistration errors. The algorithm uses a dynamic state space model of head motion with an observation equation that models continuous slice acquisition of the scanner. Under this model the particle filter provides more accurate motion estimates and voxel position estimates. We demonstrate significant performance improvement of the proposed approach as compared to registration-only methods of head motion estimation and brain activation detection.