We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a
framework for joint image registration and biophysical inversion and we apply
it to analyse MR images of glioblastomas (primary brain tumors). We have two
applications in mind. The first one is normal-to-abnormal image registration in
the presence of tumor-induced topology differences. The second one is
biophysical inversion based on single-time patient data. The underlying
optimization problem is highly non-linear and non-convex and has not been
solved before with a gradient-based approach. Given the segmentation of a
normal brain MRI and the segmentation of a cancer patient MRI, we determine
tumor growth parameters and a registration map so that if we ``grow a tumor''
(using our tumor model) in the normal brain and then register it to the patient
image, then the registration mismatch is as small as possible. This
``$\backslash$emphcoupled problem'' two-way couples the biophysical inversion and the
registration problem. In the image registration step we solve a
large-deformation diffeomorphic registration problem parameterized by an
Eulerian velocity field. In the biophysical inversion step we estimate
parameters in a reaction-diffusion tumor growth model that is formulated as a
partial differential equation (PDE). In SIBIA, we couple these two
sub-components in an iterative manner. We first presented the components of
SIBIA in ``Gholami et al, Framework for Scalable Biophysics-based Image
Analysis, IEEE/ACM Proceedings of the SC2017'', in which we derived parallel
distributed memory algorithms and software modules for the decoupled
registration and biophysical inverse problems.
In this paper, our contributions are the introduction of a PDE-constrained
optimization formulation of the coupled problem, and the derivation of a Picard
iterative solution scheme. We perform extensive tests to experimentally assess
the performance of our method on synthetic and clinical datasets. We
demonstrate the convergence of the SIBIA optimization solver in different usage
scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled
problem in three dimensions (256^3 resolution) in a few minutes using 11
dual-x86 nodes.
%0 Journal Article
%1 scheufele2019coupling
%A Scheufele, Klaudius
%A Mang, Andreas
%A Gholami, Amir
%A Davatzikos, Christos
%A Biros, George
%A Mehl, Miriam
%D 2019
%E Elsevier,
%I Elsevier
%J Computer Methods in Applied Mechanics and Engineering
%K biophysically_constrained_diffeomorphic_image_registration;_tumor_growth;_atlas_registration;_adjoint-based_methods;_parallel_algorithms from:ajaust
%P 1--34
%R https://doi.org/10.1016/j.cma.2018.12.008
%T Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration
%U http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-01&engl=0
%X We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a
framework for joint image registration and biophysical inversion and we apply
it to analyse MR images of glioblastomas (primary brain tumors). We have two
applications in mind. The first one is normal-to-abnormal image registration in
the presence of tumor-induced topology differences. The second one is
biophysical inversion based on single-time patient data. The underlying
optimization problem is highly non-linear and non-convex and has not been
solved before with a gradient-based approach. Given the segmentation of a
normal brain MRI and the segmentation of a cancer patient MRI, we determine
tumor growth parameters and a registration map so that if we ``grow a tumor''
(using our tumor model) in the normal brain and then register it to the patient
image, then the registration mismatch is as small as possible. This
``$\backslash$emphcoupled problem'' two-way couples the biophysical inversion and the
registration problem. In the image registration step we solve a
large-deformation diffeomorphic registration problem parameterized by an
Eulerian velocity field. In the biophysical inversion step we estimate
parameters in a reaction-diffusion tumor growth model that is formulated as a
partial differential equation (PDE). In SIBIA, we couple these two
sub-components in an iterative manner. We first presented the components of
SIBIA in ``Gholami et al, Framework for Scalable Biophysics-based Image
Analysis, IEEE/ACM Proceedings of the SC2017'', in which we derived parallel
distributed memory algorithms and software modules for the decoupled
registration and biophysical inverse problems.
In this paper, our contributions are the introduction of a PDE-constrained
optimization formulation of the coupled problem, and the derivation of a Picard
iterative solution scheme. We perform extensive tests to experimentally assess
the performance of our method on synthetic and clinical datasets. We
demonstrate the convergence of the SIBIA optimization solver in different usage
scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled
problem in three dimensions (256^3 resolution) in a few minutes using 11
dual-x86 nodes.
@article{scheufele2019coupling,
abstract = {We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a
framework for joint image registration and biophysical inversion and we apply
it to analyse MR images of glioblastomas (primary brain tumors). We have two
applications in mind. The first one is normal-to-abnormal image registration in
the presence of tumor-induced topology differences. The second one is
biophysical inversion based on single-time patient data. The underlying
optimization problem is highly non-linear and non-convex and has not been
solved before with a gradient-based approach. Given the segmentation of a
normal brain MRI and the segmentation of a cancer patient MRI, we determine
tumor growth parameters and a registration map so that if we ``grow a tumor''
(using our tumor model) in the normal brain and then register it to the patient
image, then the registration mismatch is as small as possible. This
``$\backslash$emph{coupled problem}'' two-way couples the biophysical inversion and the
registration problem. In the image registration step we solve a
large-deformation diffeomorphic registration problem parameterized by an
Eulerian velocity field. In the biophysical inversion step we estimate
parameters in a reaction-diffusion tumor growth model that is formulated as a
partial differential equation (PDE). In SIBIA, we couple these two
sub-components in an iterative manner. We first presented the components of
SIBIA in ``Gholami et al, Framework for Scalable Biophysics-based Image
Analysis, IEEE/ACM Proceedings of the SC2017'', in which we derived parallel
distributed memory algorithms and software modules for the decoupled
registration and biophysical inverse problems.
In this paper, our contributions are the introduction of a PDE-constrained
optimization formulation of the coupled problem, and the derivation of a Picard
iterative solution scheme. We perform extensive tests to experimentally assess
the performance of our method on synthetic and clinical datasets. We
demonstrate the convergence of the SIBIA optimization solver in different usage
scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled
problem in three dimensions (256^3 resolution) in a few minutes using 11
dual-x86 nodes.},
added-at = {2020-07-27T15:19:28.000+0200},
author = {Scheufele, Klaudius and Mang, Andreas and Gholami, Amir and Davatzikos, Christos and Biros, George and Mehl, Miriam},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/235380753f4a5228c9fc4ac994969315b/ipvs-sgs},
cr-category = {G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
J.3 Life and Medical Sciences},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
doi = {https://doi.org/10.1016/j.cma.2018.12.008},
editor = {Elsevier},
ee = {https://arxiv.org/abs/1710.06420},
interhash = {c080196d1265ab06923cba3bb4561cd0},
intrahash = {35380753f4a5228c9fc4ac994969315b},
journal = {Computer Methods in Applied Mechanics and Engineering},
keywords = {biophysically_constrained_diffeomorphic_image_registration;_tumor_growth;_atlas_registration;_adjoint-based_methods;_parallel_algorithms from:ajaust},
language = {Englisch},
month = {Januar},
pages = {1--34},
publisher = {Elsevier},
timestamp = {2020-07-27T14:10:33.000+0200},
title = {{Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration}},
type = {Artikel in Zeitschrift},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-01&engl=0},
year = 2019
}