Temporal Registration in Application to In-utero MRI Time Series

TL;DR

This paper introduces a robust temporal registration method for in-utero MRI time series that leverages a hidden Markov model to improve motion correction by exploiting temporal smoothness and small inter-frame motion.

Contribution

The novel approach applies a Markov assumption and HMM-based message passing to enhance motion correction in fetal MRI sequences, outperforming traditional registration methods.

Findings

Accurately captures temporal dynamics of fetal motion.

Improves alignment accuracy over existing methods.

Enhances segmentation propagation in MRI time series.

Abstract

We present a robust method to correct for motion in volumetric in-utero MRI time series. Time-course analysis for in-utero volumetric MRI time series often suffers from substantial and unpredictable fetal motion. Registration provides voxel correspondences between images and is commonly employed for motion correction. Current registration methods often fail when aligning images that are substantially different from a template (reference image). To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We evaluate the utility of the temporal model in the context of in-utero MRI time series alignment by examining the accuracy of propagated segmentation label maps. Our results suggest that the proposed model captures accurately the temporal dynamics of transformations in in-utero MRI time series.