A stabilized multigrid solver for hyperelastic image registration

Summary Image registration is a central problem in a variety of areas involving imaging techniques and is known to be challenging and ill‐posed. Regularization functionals based on hyperelasticity provide a powerful mechanism for limiting the ill‐posedness. A key feature of hyperelastic image regist...

Full description

Saved in:
Bibliographic Details
Published in:Numerical linear algebra with applications Vol. 24; no. 5
Main Authors: Ruthotto, Lars, Greif, Chen, Modersitzki, Jan
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.10.2017
Wiley Subscription Services, Inc
Subjects:
Deformation mechanisms
Formability
Fourier analysis
Functionals
Galerkin method
Gauss-Newton method
Ill posed problems
Image registration
image registration, biomedical imaging
Imaging techniques
Mathematical models
multigrid methods
Nonlinear programming
numerical optimization
Partial differential equations
Registration
Regularization
ISSN:1070-5325, 1099-1506
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary Image registration is a central problem in a variety of areas involving imaging techniques and is known to be challenging and ill‐posed. Regularization functionals based on hyperelasticity provide a powerful mechanism for limiting the ill‐posedness. A key feature of hyperelastic image registration approaches is their ability to model large deformations while guaranteeing their invertibility, which is crucial in many applications. To ensure that numerical solutions satisfy this requirement, we discretize the variational problem using piecewise linear finite elements, and then solve the discrete optimization problem using the Gauss–Newton method. In this work, we focus on computational challenges arising in approximately solving the Hessian system. We show that the Hessian is a discretization of a strongly coupled system of partial differential equations whose coefficients can be severely inhomogeneous. Motivated by a local Fourier analysis, we stabilize the system by thresholding the coefficients. We propose a Galerkin‐multigrid scheme with a collective pointwise smoother. We demonstrate the accuracy and effectiveness of the proposed scheme, first on a two‐dimensional problem of a moderate size and then on a large‐scale real‐world application with almost 9 million degrees of freedom.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1070-5325
1099-1506
DOI:10.1002/nla.2095