Temporal Registration in In-Utero Volumetric MRI Time Series
Name
nihms837305.pdf
Description
Accepted version
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571.48 KB
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Checksum (MD5)
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Author(s)
Liao, Ruizhi
Turk, Esra Abaci
Zhang, Miaomiao
Luo, Jie
Grant, P. Ellen
Adalsteinsson, Elfar
Golland, Polina
Date Issued
2016
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
Liao, Ruizhi et al. "Temporal Registration in In-Utero Volumetric MRI Time Series." MICCAI 2016: Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, 9902, Springer, 2016, 54-62. © 2016 Springer International Publishing
Version
Author's final manuscript
Abstract
We present a robust method to correct for motion and deformations in 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.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-46726-9_7
