@inproceedings {INPROC-2019-39,
author = {Klaudius Scheufele and Shashank Subramanian and Andreas Mang and George Biros and Miriam Mehl},
title = {{IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION}},
booktitle = {SIAM Journal on Scientific Computing},
publisher = {Springer},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--24},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2019},
keywords = {tumor progression inversion; biophysical model calibration; image registration; PDE constrained optimization; Picard iteration},
language = {Englisch},
cr-category = {J.3 Life and Medical Sciences},
ee = {https://arxiv.org/abs/1907.07774},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {We present a novel formulation for the calibration of a biophysical tumor
growth model from a single-time snapshot, MRI scan of a glioblastoma patient.
Tumor growth models are typically nonlinear parabolic partial differential
equations (PDEs). Thus, we have to generate a second snapshot to be able to
extract significant information from a single patient snapshot. We create this
two-snapshot scenario as follows. We use an atlas (an average of several scans
of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of
the patient. Then, using the patient scan and the atlas, we combine
image-registration algorithms and parameter estimation algorithms to achieve a
better estimate of the healthy patient scan and the tumor growth parameters
that are consistent with the data. Our scheme is based on our recent work
(Scheufele et al, ``Biophysically constrained diffeomorphic image registration,
Tumor growth, Atlas registration, Adjoint-based methods, Parallel algorithms'',
CMAME, 2018), but apply a different and novel scheme where the tumor growth
simulation in contrast to the previous work is executed in the patient brain
domain and not in the atlas domain yielding more meaningful patient-specific
results. As a basis, we use a PDE-constrained optimization framework. We derive
a modified Picard-iteration-type solution strategy in which we alternate
between registration and tumor parameter estimation in a new way. In addition,
we consider an {\^a}„“1 sparsity constraint on the initial condition for the tumor
and integrate it with the new joint inversion scheme. We solve the subproblems
with a reduced-space, inexact Gauss-Newton-Krylov/quasi-Newton methods. We
present results using real brain data with synthetic tumor data that show that
the new scheme reconstructs the tumor parameters in a more accurate and
reliable way compared to our earlier scheme.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2019-39&engl=0}
}
@inproceedings {INPROC-2017-62,
author = {Andreas Mang and Sameer Tarakan and Amir Gholami and Naveen Himthani and Subramanian and Shanshank and James Levitt and Muneeza Azmat and Klaudius Scheufele and Miriam Mehl and Christos Davatzikos and Bill Bart and George Biros},
title = {{SIBIA-GlS: Scalable Biophysics-Based Image Analysis for Glioma Segmentation}},
booktitle = {The multimodal brain tumor image segmentation benchmark (BRATS), MICCAI},
publisher = {-},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {202--209},
type = {Konferenz-Beitrag},
month = {Juli},
year = {2017},
language = {Englisch},
cr-category = {G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
J.3 Life and Medical Sciences},
ee = {https://www.cbica.upenn.edu/sbia/Spyridon.Bakas/MICCAI_BraTS/MICCAI_BraTS_2017_proceedings_shortPapers.pdf},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-62&engl=0}
}
@inproceedings {INPROC-2017-61,
author = {Amir Gholami and Andreas Mang and Klaudius Scheufele and Christos Davatzikos and Miriam Mehl and George Biros},
title = {{A Framework for Scalable Biophysics-based Image Analysis}},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC17},
address = {New York, NY, USA},
publisher = {ACM},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
pages = {1--13},
type = {Konferenz-Beitrag},
month = {November},
year = {2017},
doi = {10.1145/3126908.3126930},
isbn = {978-1-4503-5114-0},
keywords = {bio-physics based image analysis; scalable image registration},
language = {Englisch},
cr-category = {G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
J.3 Life and Medical Sciences},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/INPROC-2017-61/INPROC-2017-61.pdf,
https://dl.acm.org/citation.cfm?doid=3126908.3126930},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2017-61&engl=0}
}
@inproceedings {INPROC-2015-29,
author = {Florian Lindner and Miriam Mehl and Klaudius Scheufele and Benjamin Uekermann},
title = {{A Comparison of various Quasi-Newton Schemes for Partitioned Fluid-Structure Interaction}},
booktitle = {Coupled Problems},
publisher = {ECCOMAS},
institution = {Universit{\"a}t Stuttgart, Fakult{\"a}t Informatik, Elektrotechnik und Informationstechnik, Germany},
type = {Konferenz-Beitrag},
month = {Januar},
year = {2015},
language = {Deutsch},
cr-category = {I.6 Simulation and Modeling},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {leer},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INPROC-2015-29&engl=0}
}
@article {ART-2020-08,
author = {Shashank Subramanian and Klaudius Scheufele and Miriam Mehl and George Biros},
title = {{Where did the tumor start? An inverse solver with sparse localization for tumor growth models}},
journal = {Inverse Problems},
publisher = {IOP Publisher},
volume = {36},
number = {4},
type = {Artikel in Zeitschrift},
month = {Februar},
year = {2020},
isbn = {10.1088/1361-6420/ab649c},
language = {Englisch},
cr-category = {G.1.2 Numerical Analysis Approximation,
G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
I.4 Image Processing and Computer Vision,
I.6.8 Types of Simulation,
J.3 Life and Medical Sciences},
ee = {https://iopscience.iop.org/article/10.1088/1361-6420/ab649c,
https://arxiv.org/abs/1907.06564},
contact = {miriam.mehl@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {We present a numerical scheme for solving an inverse problem for parameter
estimation in tumor growth models for glioblastomas, a form of aggressive
primary brain tumor. The growth model is a reaction–diffusion partial
differential equation (PDE) for the tumor concentration. We use a
PDE-constrained optimization formulation for the inverse problem. The unknown
parameters are the reaction coefficient (proliferation), the diffusion
coefficient (infiltration), and the initial condition field for the tumor PDE.
Segmentation of magnetic resonance imaging (MRI) scans drive the inverse
problem where segmented tumor regions serve as partial observations of the
tumor concentration. Like most cases in clinical practice, we use data from a
single time snapshot. Moreover, the precise time relative to the initiation of
the tumor is unknown, which poses an additional difficulty for inversion. We
perform a frozen-coefficient spectral analysis and show that the inverse
problem is severely ill-posed. We introduce a biophysically motivated
regularization on the structure and magnitude of the tumor initial condition.
In particular, we assume that the tumor starts at a few locations (enforced
with a sparsity constraint on the initial condition of the tumor) and that the
initial condition magnitude in the maximum norm is equal to one. We solve the
resulting optimization problem using an inexact quasi-Newton method combined
with a compressive sampling algorithm for the sparsity constraint. Our
implementation uses PETSc and AccFFT libraries. We conduct numerical
experiments on synthetic and clinical images to highlight the improved
performance of our solver over a previously existing solver that uses standard
two-norm regularization for the calibration parameters. The existing solver is
unable to localize the initial condition. Our new solver can localize the
initial condition and recover infiltration and proliferation. In clinical
datasets (for which the ground truth is unknown), our solver results in
qualitatively different solutions compared to the two-norm regularized solver.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2020-08&engl=0}
}
@article {ART-2019-01,
author = {Klaudius Scheufele and Andreas Mang and Amir Gholami and Christos Davatzikos and George Biros and Miriam Mehl},
title = {{Coupling Brain-Tumor Biophysical Models and Diffeomorphic Image Registration}},
journal = {Computer Methods in Applied Mechanics and Engineering},
editor = {Elsevier},
publisher = {Elsevier},
pages = {1--34},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2019},
doi = {https://doi.org/10.1016/j.cma.2018.12.008},
keywords = {biophysically constrained diffeomorphic image registration; tumor growth; atlas registration; adjoint-based methods; parallel algorithms},
language = {Englisch},
cr-category = {G.1.6 Numerical Analysis Optimization,
G.1.8 Partial Differential Equations,
J.3 Life and Medical Sciences},
ee = {https://arxiv.org/abs/1710.06420},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
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.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2019-01&engl=0}
}
@article {ART-2017-11,
author = {Klaudius Scheufele and Miriam Mehl},
title = {{ROBUST MULTI-SECANT QUASI-NEWTON VARIANTS FOR PARALLEL FLUID-STRUCTURE SIMULATIONS—AND OTHER MULTIPHYSICS APPLICATIONS}},
journal = {Siam Journal on Scientific Computing, Volume 39, Issue 5},
editor = {SIAM},
publisher = {SIAM},
volume = {39},
number = {5},
pages = {404--433},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2017},
isbn = {10.1137/16M1082020},
keywords = {partitioned multiphysics; nonlinear fixed-point solver; quasi-Newton, fluid-structure interaction},
language = {Englisch},
cr-category = {G.4 Mathematical Software,
G.1.6 Numerical Analysis Optimization},
ee = {ftp://ftp.informatik.uni-stuttgart.de/pub/library/ncstrl.ustuttgart_fi/ART-2017-11/ART-2017-11.pdf},
contact = {klaudius.scheufele@ipvs.uni-stuttgart.de},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2017-11&engl=0}
}
@article {ART-2016-02,
author = {Hans-Joachim Bungartz and Florian Lindner and Bernhard Gatzhammer and Miriam Mehl and Klaudius Scheufele and Alexander Shukaev and Benjamin Uekermann},
title = {{preCICE – A Fully Parallel Library for Multi-Physics Surface Coupling}},
journal = {Computers \& Fluids},
publisher = {Elsevier},
pages = {1--1},
type = {Artikel in Zeitschrift},
month = {Januar},
year = {2016},
doi = {http://dx.doi.org/10.1016/j.compfluid.2016.04.003},
issn = {0045-7930},
keywords = {partitioned multi-physics; strong coupling; non-matching grids; inter-code communication; quasi-Newton; radial basis functions; high performance computing},
language = {Deutsch},
cr-category = {G.1.0 Numerical Analysis General,
D.0 Software General},
ee = {http://www.sciencedirect.com/science/article/pii/S0045793016300974},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {Abstract In the emerging field of multi-physics simulations, we often face the
challenge to establish new connections between physical fields, to add
additional aspects to existing models, or to exchange a solver for one of the
involved physical fields. If in such cases a fast prototyping of a coupled
simulation environment is required, a partitioned setup using existing codes
for each physical field is the optimal choice. As accurate models require also
accurate numerics, multi-physics simulations typically use very high grid
resolutions and, accordingly, are run on massively parallel computers. Here, we
face the challenge to combine flexibility with parallel scalability and
hardware efficiency. In this paper, we present the coupling tool preCICE which
offers the complete coupling functionality required for a fast development of a
multi-physics environment using existing, possibly black-box solvers. We hereby
restrict ourselves to bidirectional surface coupling which is too expensive to
be done via file communication, but in contrast to volume coupling still a
candidate for distributed memory parallelism between the involved solvers. The
paper gives an overview of the numerical functionalities implemented in preCICE
as well as the user interfaces, i.e., the application programming interface and
configuration options. Our numerical examples and the list of different
open-source and commercial codes that have already been used with preCICE in
coupled simulations show the high flexibility, the correctness, and the high
performance and parallel scalability of coupled simulations with preCICE as the
coupling unit.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=ART-2016-02&engl=0}
}
@inbook {INBOOK-2016-06,
author = {Hans-Joachim Bungartz and Florian Lindner and Mehl Miriam and Klaudius Scheufele and Alexander Shukaev and Benjamin Uekermann},
title = {{Partitioned Fluid–Structure–Acoustics Interaction on Distributed Data: Coupling via preCICE}},
series = {Software for Exascale Computing - SPPEXA 2013-2015},
publisher = {Springer International Publishing},
pages = {239--266},
type = {Beitrag in Buch},
month = {Januar},
year = {2016},
isbn = {978-3-319-40528-5},
doi = {10.1007/978-3-319-40528-5_11},
keywords = {preCICE},
language = {Englisch},
cr-category = {D.0 Software General},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {One of the great prospects of exascale computing is to simulate chal- lenging
highly complex multi-physics scenarios with different length and time scales. A
modular approach re-using existing software for the single-physicsmodel parts
has great advantages regarding flexibility and software development costs. At
the same time, it poses challenges in terms of numerical stability and parallel
scalability. The coupling library preCICE provides communication, data mapping,
and coupling numerics for surface-coupled multi-physics applications in a
highly modular way.We recapitulate the numerical methods but focus particularly
on their parallel implementation. The numerical results for an artificial
coupling interface showa very small runtime of the coupling compared to typical
solver runtimes and a good parallel scalability on a number of cores
corresponding to amassively parallel simulation for an actual, coupled
simulation. Further results for actual application scenarios from the field of
fluid-structure-acoustic interactions are presented in the next chapter.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INBOOK-2016-06&engl=0}
}
@inbook {INBOOK-2015-07,
author = {David Blom and Florian Lindner and Miriam Mehl and Klaudius Scheufele and Alexander van Zuijlen},
title = {{A Review on Fast Quasi-Newton and Accelerated Fixed Point Iterations for Partitioned Fluid-Structure Interaction Simulation}},
series = {Advances in Computational Fluid-Structure Interaction},
publisher = {Springer International Publishing},
series = {Modeling and Simulation in Science, Engineering and Technology},
pages = {1--12},
type = {Beitrag in Buch},
month = {Januar},
year = {2015},
isbn = {978-3-319-40827-9},
isbn = {978-3-319-40825-5},
language = {Englisch},
cr-category = {I.6 Simulation and Modeling},
department = {Universit{\"a}t Stuttgart, Institut f{\"u}r Parallele und Verteilte Systeme, Simulation gro{\ss}er Systeme},
abstract = {The partitioned simulation of fluid{\^a}€“structure interactions offers great
flexibility in terms of exchanging flow and structure solver and using existing
established codes. However, it often suffers from slow convergence and limited
parallel scalability. Quasi-Newton or accelerated fixed-point iterations are a
very efficient way to solve the convergence issue. At the same time, they
stabilize and speed up not only the standard staggered fluid{\^a}€“structure
coupling iterations, but also the variant with simultaneous execution of flow
and structure solver that is fairly inefficient if no acceleration methods for
the underlying fixed-point iteration are used. In this chapter, we present a
review on combinations of iteration patterns (parallel and staggered) and of
quasi-Newton methods and compare their suitability in terms of convergence
speed, robustness, and parallel scalability. Some of these variants use the
so-called manifold mapping that yields an additional speedup by using an
approach that can be interpreted as a generalization of the multi-level idea.},
url = {http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=INBOOK-2015-07&engl=0}
}