Parallel scalable PDE-constrained optimization: antenna identification in hyperthermia cancer treatment planning

Schenk, Olaf; Manguoğlu, Murat; Sameh, Ahmed; Christen, Matthias; Sathe, Madan

doi:10.1007/s00450-009-0080-x

Cited by 23 publications

(14 citation statements)

References 22 publications

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“…Several strategies to efficiently solve these equations have been considered; they range from conditionally stable total variation diminishing [26], second and fourthorder Runge-Kutta [82,83,102], and high-order essentially nonoscillatory [62] schemes, to unconditionally stable implicit Lax-Friedrich [18,114], SL [16,30,85,89], and Lagrangian [87] schemes. We use a SL scheme.Examples for parallel solvers for PDE-constrained optimization problems can be found in [3,4,19,20,[22][23][24]113]. We refer to [39,44,110,112] for surveys on parallel algorithms for image registration.…”

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confidence: 99%

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Mang

Gholami

Davatzikos

et al. 2019

SIAM J. Sci. Comput.

112

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We introduce CLAIRE, a distributed-memory algorithm and software for solving constrained large deformation diffeomorphic image registration problems in three dimensions. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme.We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousand of distributed memory nodes on high-end clusters. Our improved, parallel implementation features parameter-, scale-, and grid-continuation schemes to speedup the computations and reduce the likelihood to get trapped in local minima. We also implement an improved preconditioner for the reduced space Hessian to speedup the convergence.We test registration performance on synthetic and real data. We demonstrate registration accuracy on 16 neuroimaging datasets. We compare the performance of our scheme against different flavors of the DEMONS algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work. Introduction.Deformable registration is a key technology in the medical imaging. It is about computing a map y that establishes a meaningful spatial correspondence between two (or more) images m R (the reference (fixed) image) and m T (the template (deformable or moving) image; image to be registered) of the same scene [43,96]. Numerous approaches for formulating and solving image registration problems have appeared in the past; we refer to [43,60,96,97,116] for lucid overviews. Image registration is typically formulated as a variational optimization problem that consists of a data fidelity term and a Tikhonov regularization functional to over-come ill-posedness [40,43]. A key concern is that y is a diffeomorphism, i.e., the map y is differentiable, a bijection, and has a differentiable inverse. We require that the determinant of the deformation gradient det ∇y does not vanish or change sign. An intuitive approach to safeguard against nondiffeomorphic maps y is to add hard and/or soft constraints on det ∇y to the variational optimization problem [29,58,103,108]. An alternative strategy to ensure regularity is to introduce a pseudo-time variable t and invert for a smooth velocity field v that encodes the map y [16,37,94,126]; existence of a diffeomorphism y can be guaranteed if v is adequately smooth [16,30,37,121]. This model is, as opposed to many traditional formulations that directly invert for y [58,88,95,108], adequate for recovering large, highly nonlinear deformations. Our approach falls into this category. T...

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confidence: 99%

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Mang

Gholami

Davatzikos

et al. 2019

SIAM J. Sci. Comput.

112

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“…At the other hand, the classical finite difference approach can also take benefit from an implementation on GPU implementation (e.g. Laplacian filtering) or on distributed architectures [25]. However, this later implementation requires specialized architectures and algorithms.…”

Section: Comparison With the Finite Difference Methodsmentioning

confidence: 99%

Fast FFT-based bioheat transfer equation computation

Dillenseger

Esneault

2010

Computers in Biology and Medicine

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This paper describes a modeling method of the tissue temperature evolution over time in hyper or hypothermia. The tissue temperature evolution over time is classically described by Pennes' bioheat transfer equation which is generally solved by a finite difference method. In this paper we will present a method where the bioheat transfer equation can be algebraically solved after a Fourier transformation over the space coordinates. As an example, we implemented this method for the simulation of a percutaneous high intensity ultrasound hepatocellular carcinoma curative treatment and compared it with the finite difference method and experimental data.

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“…Second variation has been implemented in [23,27,41] and is called PSPIKE. We obtained the g3_circuit (1,585,478 unknowns and 7,660,826 nonzeros) matrix from the University of Florida Sparse Matrix Collection.…”

Section: Form the Schur Complementmentioning

confidence: 99%

Parallel Solution of Sparse Linear Systems

Manguoğlu

2012

High-Performance Scientific Computing

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Many simulations in science and engineering give rise to sparse linear systems of equations. It is a well known fact that the cost of the simulation process is almost always governed by the solution of the linear systems especially for largescale problems. The emergence of extreme-scale parallel platforms, along with the increasing number of processing cores available on a single chip pose significant challenges for algorithm development. Machines with tens of thousands of multicore processors place tremendous constraints on the communication as well as memory access requirements of algorithms. The increase in number of cores in a processing unit without an increase in memory bandwidth aggravates an already significant memory bottleneck. Sparse linear algebra kernels are well-known for their poor processor utilization. This is a result of limited memory reuse, which renders data caching less effective. In view of emerging hardware trends, it is necessary to develop algorithms that strike a more meaningful balance between memory accesses, communication, and computation. Specifically, an algorithm that performs more floating point operations at the expense of reduced memory accesses and communication is likely to yield better performance. We present two alternative variations of DS factorization based methods for solution of sparse linear systems on parallel computing platforms. Performance comparisons to traditional LU factorization based parallel solvers are also discussed. We show that combining iterative methods with direct solvers and using DS factorization, one can achieve better scalability and shorter time to solution. IntroductionMany simulations in science and engineering give rise to sparse linear systems of equations. It is a well known fact that the cost of the solution process is almost always governed by the solution of the linear systems especially for large-scale problems. The emergence of extreme-scale parallel platforms, along with the increasing number of processing cores available on a single chip pose significant challenges M. Manguoglu ( )

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Parallel scalable PDE-constrained optimization: antenna identification in hyperthermia cancer treatment planning

Cited by 23 publications

References 22 publications

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

CLAIRE: A Distributed-Memory Solver for Constrained Large Deformation Diffeomorphic Image Registration

Fast FFT-based bioheat transfer equation computation

Parallel Solution of Sparse Linear Systems

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