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Frequency Diffeomorphisms for Efficient Image Registration
Name
nihms934488.pdf
Description
Accepted version
Size
687.74 KB
Format
Adobe PDF
Checksum (MD5)
c71466b24f73818b944de5e74cbcd01e
Author(s)
Zhang, Miaomiao
Liao, Ruizhi
Dalca, Adrian V.
Turk, Esra A.
Luo, Jie
Grant, P. Ellen
Golland, Polina
Date Issued
2017
Publisher
Springer Nature
Citation
Zhang, Miaomiao, Liao, Ruizhi, Dalca, Adrian V., Turk, Esra A., Luo, Jie et al. 2017. "Frequency Diffeomorphisms for Efficient Image Registration."
Version
Author's final manuscript
Abstract
© Springer International Publishing AG 2017. This paper presents an efficient algorithm for large deformation diffeomorphic metric mapping (LDDMM) with geodesic shooting for image registration. We introduce a novel finite dimensional Fourier representation of diffeomorphic deformations based on the key fact that the high frequency components of a diffeomorphism remain stationary throughout the integration process when computing the deformation associated with smooth velocity fields. We show that manipulating high dimensional diffeomorphisms can be carried out entirely in the bandlimited space by integrating the nonstationary low frequency components of the displacement field. This insight substantially reduces the computational cost of the registration problem. Experimental results show that our method is significantly faster than the state-of-the-art diffeomorphic image registration methods while producing equally accurate alignment. We demonstrate our algorithm in two different applications of image registration: neuroimaging and in-utero imaging.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1007/978-3-319-59050-9_44
