# Temporal Registration in In-Utero Volumetric MRI Time Series

arXiv: https://arxiv.org/abs/1608.03907 · HTML: https://mlsanity.com/paper/1608.03907/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [TLRN: Temporal Latent Residual Networks For Large Deformation Image Registration](https://mlsanity.com/paper/2407.11219/) (2024)
- [SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking](https://mlsanity.com/paper/2407.20198/) (2024)

## Abstract (arXiv)

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.