Temporal Registration in In-Utero Volumetric MRI Time Series

TL;DR

This paper introduces a robust temporal registration method for in-utero volumetric MRI time series, leveraging a hidden Markov model to improve alignment and segmentation accuracy amidst unpredictable motion.

Contribution

The paper proposes a novel Markov-based model for temporal registration that effectively accounts for small inter-frame motions in in-utero MRI sequences.

Findings

Improved segmentation propagation accuracy.

Effective modeling of in-utero motion dynamics.

Enhanced alignment robustness in dynamic MRI.

Abstract

We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure 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 demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.