Temporal Registration in In-Utero Volumetric MRI Time Series
Author(s)Grant, P. Ellen; Liao, Ruizhi; Abaci Turk, Esra; Zhang, Miaomiao; Luo, Jie; Adalsteinsson, Elfar; Golland, Polina; ... Show more Show less
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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.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Research Laboratory of Electronics
19th International Conference on Medical Image Computing & Computer Assisted Intervention, MICCAI'16
Liao, Ruizhi, Esra A. Turk, Miaomiao Zhang, Jie Luo, P. Ellen Grant, Elfar Adalsteinsson, and Polina Golland. "Temporal Registration in In-Utero Volumetric MRI Time Series." MICCA'16, 19th International Conference on Medical Image Computing & Computer Assisted Intervention, October 17-21, 2016, Athens, Greece.
Author's final manuscript