An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging

An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate... We present an efficient numerical method to quantify the spatial variation of glioma growth based on subject-specific medical images using a mechanically-coupled tumor model. The method is illustrated in a murine model of glioma in which we consider the tumor as a growing elastic mass that continuously deforms the surrounding healthy-appearing brain tissue. As an inverse parameter identification problem, we quantify the volumetric growth of glioma and the growth component of deformation by fitting the model predicted cell density to the cell density estimated using the diffusion-weighted magnetic resonance imaging data. Numerically, we developed an adjoint-based approach to solve the optimization problem. Results on a set of experimentally measured, in vivo rat glioma data indicate good agreement between the fitted and measured tumor area and suggest a wide variation of in-plane glioma growth with the growth-induced Jacobian ranging from 1.0 to 6.0. Keywords Glioma growth · Mechanically-coupled · Inverse problem · Finite element · Adjoint-state method · Mass effect 1 Introduction to the observation. However, these empirical models are nei- ther able to quantify the spatial variation of tumor growth Tumor growth is a complex phenomenon affected by a nor to provide insights into the underlying biological mech- series http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Mechanics Springer Journals

An adjoint-based method for a linear mechanically-coupled tumor model: application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Theoretical and Applied Mechanics; Computational Science and Engineering; Classical and Continuum Physics
ISSN
0178-7675
eISSN
1432-0924
D.O.I.
10.1007/s00466-018-1589-2
Publisher site
See Article on Publisher Site

Abstract

We present an efficient numerical method to quantify the spatial variation of glioma growth based on subject-specific medical images using a mechanically-coupled tumor model. The method is illustrated in a murine model of glioma in which we consider the tumor as a growing elastic mass that continuously deforms the surrounding healthy-appearing brain tissue. As an inverse parameter identification problem, we quantify the volumetric growth of glioma and the growth component of deformation by fitting the model predicted cell density to the cell density estimated using the diffusion-weighted magnetic resonance imaging data. Numerically, we developed an adjoint-based approach to solve the optimization problem. Results on a set of experimentally measured, in vivo rat glioma data indicate good agreement between the fitted and measured tumor area and suggest a wide variation of in-plane glioma growth with the growth-induced Jacobian ranging from 1.0 to 6.0. Keywords Glioma growth · Mechanically-coupled · Inverse problem · Finite element · Adjoint-state method · Mass effect 1 Introduction to the observation. However, these empirical models are nei- ther able to quantify the spatial variation of tumor growth Tumor growth is a complex phenomenon affected by a nor to provide insights into the underlying biological mech- series

Journal

Computational MechanicsSpringer Journals

Published: Jun 2, 2018

References

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