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Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Orthotropic Thin Shell Elasticity Estimation for Surface Registration

Authors : Qingyu Zhao, Stephen Pizer, Ron Alterovitz, Marc Niethammer, Julian Rosenman

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Elastic physical models have been widely used to regularize deformations in different medical object registration tasks. Traditional approaches usually assume uniform isotropic tissue elasticity (a constant regularization weight) across the whole domain, which contradicts human tissue elasticity being not only inhomogeneous but also anisotropic. We focus on producing more physically realistic deformations for the task of surface registration. We model the surface as an orthotropic elastic thin shell, and we propose a novel statistical framework to estimate inhomogeneous and anisotropic shell elasticity parameters only from a group of known surface deformations. With this framework we show that a joint estimation of within-patient surface deformations and the shell elasticity parameters can improve groupwise registration accuracy. The method is tested in the context of endoscopic reconstruction-surface registration.

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previous chapter HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics
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Metadata
Title
Orthotropic Thin Shell Elasticity Estimation for Surface Registration
Authors
Qingyu Zhao
Stephen Pizer
Ron Alterovitz
Marc Niethammer
Julian Rosenman
Copyright Year
2017
Book
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_39

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