Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1903.02959 | 0 | 0.34 |
References | Authors | |
14 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruizhi Liao | 1 | 8 | 2.36 |
| Esra A. Turk | 2 | 4 | 3.10 |
| Miaomiao Zhang | 3 | 132 | 26.12 |
| Jie Luo | 4 | 12 | 5.58 |
| Elfar Adalsteinsson | 5 | 129 | 16.18 |
| P Ellen Grant | 6 | 205 | 20.81 |
| Polina Golland | 7 | 1690 | 114.38 |