Registration Uncertainty Quantification Via Low-dimensional Characterization of Geometric Deformations
Name
nihms-1553600.pdf
Description
Accepted version
Size
1.61 MB
Format
Adobe PDF
Checksum (MD5)
531446dc4ade4c63fdcb182a1c9ef96d
Author(s)
Wells, William M.
Date Issued
December 2019
Journal
Magnetic Resonance Imaging
Publisher
Elsevier BV
Citation
Wang, Jian et al. “Registration Uncertainty Quantification Via Low-dimensional Characterization of Geometric Deformations.” Magnetic Resonance Imaging, 64 (December 2019): 122–131 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
This paper presents an efficient approach to quantifying image registration uncertainty based on a low-dimensional representation of geometric deformations. In contrast to previous methods, we develop a Bayesian diffeomorphic registration framework in a bandlimited space, rather than a high-dimensional image space. We show that a dense posterior distribution on deformation fields can be fully characterized by much fewer parameters, which dramatically reduces the computational complexity of model inferences. To further avoid heavy computation loads introduced by random sampling algorithms, we approximate a marginal posterior by using Laplace's method at the optimal solution of log-posterior distribution. Experimental results on both 2D synthetic data and real 3D brain magnetic resonance imaging (MRI) scans demonstrate that our method is significantly faster than the state-of-the-art diffeomorphic registration uncertainty quantification algorithms, while producing comparable results.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.MRI.2019.05.034
