Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation

@article{Ashburner2011DiffeomorphicRU,
  title={Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation},
  author={John Ashburner and Karl J. Friston},
  journal={Neuroimage},
  year={2011},
  volume={55},
  pages={954 - 967}
}
Stuttgart Efficient Algorithms for Geodesic Shooting in Diffeomorphic Image Registration
TLDR
It is demonstrated that low rank preconditioners are able to significantly reduce the number of iterations needed to solve the linear system in Newton-type optimizers, and different optimization methods are compared based on their overall performance.
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TLDR
This paper introduces Fourier-approximated Lie algebras for shooting (FLASH), a fast geodesic shooting algorithm for diffeomorphic image registration that approximate the infinite-dimensional Lie algebra of smooth vector fields with a low-dimensional, bandlimited space.
Probabilistic Diffeomorphic Registration: Representing Uncertainty
TLDR
A novel mathematical framework for representing uncertainty in large deformation diffeomorphic image registration by estimating the full posterior distribution in order to represent uncertainty, in contrast to methods in which the posterior is approximated via Monte Carlo sampling or maximized in maximum a-posteriori (MAP) estimation.
Adaptive gradient descent optimization of initial momenta for geodesic shooting in diffeomorphisms
  • G. Fleishman, P. Thompson
  • Computer Science
    2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
  • 2017
TLDR
It is found that the Barzilai-Borwein method with a backtracking line search outperforms other optimization schemes in convergence time and adaptability by a wide margin and that the variable optimization schemes do not significantly affect the ability to measure atrophy with clinical significance.
Diffeomorphic Metric Landmark Mapping Using Stationary Velocity Field Parameterization
TLDR
Compared to non-stationary parameterization, this method can achieve comparable registration accuracy in significantly reduced time, and second order metric approximation by this method improves significantly over first order, which can not be achieved by non- stationary parameterized.
An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration
TLDR
Overall, the reported results demonstrate excellent numerical accuracy, guaranteed local deformation regularity, and computational efficiency with an optional control on local mass conservation with a black-box solver that exploits computational tools that are precisely tailored for solving the optimality system.
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