Temporal Registration in In-Utero Volumetric MRI Time Series

• Main Authors: Grant, P. Ellen (Author), Liao, Ruizhi (Contributor), Abaci Turk, Esra (Contributor), Zhang, Miaomiao (Contributor), Luo, Jie (Contributor), Adalsteinsson, Elfar (Contributor), Golland, Polina (Contributor)
• Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Research Laboratory of Electronics (Contributor)
• Format: Article
• Language: English
• Published: MICCAI Society, 2016-10-28T14:46:15Z.
• Subjects: Article
• Online Access: Get fulltext

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.
National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)
National Institutes of Health (U.S.) (NIH NICHD U01HD087211)
National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)
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