Chapter title |
Temporal Registration in In-Utero Volumetric MRI Time Series
|
---|---|
Chapter number | 7 |
Book title |
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46726-9_7 |
Pubmed ID | |
Book ISBNs |
978-3-31-946725-2, 978-3-31-946726-9
|
Authors |
Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, P. Ellen Grant, Elfar Adalsteinsson, Polina Golland |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 5% |
United States | 1 | 5% |
Unknown | 18 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 35% |
Researcher | 5 | 25% |
Student > Doctoral Student | 2 | 10% |
Student > Postgraduate | 1 | 5% |
Unknown | 5 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 7 | 35% |
Computer Science | 3 | 15% |
Medicine and Dentistry | 3 | 15% |
Physics and Astronomy | 2 | 10% |
Unknown | 5 | 25% |