Parallel simulation of anisotropic diffusion with human brain DT-MRI data

Ning Kang; Jun Zhang; Eric S. Carlson

doi:10.1016/j.compstruc.2004.04.011

Parallel simulation of anisotropic diffusion with human brain DT-MRI data

Ning Kang, Jun Zhang, Eric S. Carlson

Computer Science

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

We conduct simulations for the 3D unsteady state anisotropic diffusion process with DT-MRI data in the human brain by discretizing the governing diffusion equation on Cartesian grid and adopting a high performance differential-algebraic equation (DAE) solver, the parallel version of implicit differential-algebraic (IDA) solver, to tackle the resulting large scale system of DAEs. Parallel preconditioning techniques including sparse approximate inverse and banded-block-diagonal preconditioners are used with the GMRES method to accelerate the convergence rate of the iterative solution. We then investigate and compare the efficiency and effectiveness of the two parallel preconditioners. The experimental results of the diffusion simulations on a parallel supercomputer show that the sparse approximate inverse preconditioning strategy, which is robust and efficient with good scalability, gives a much better overall performance than the banded-block-diagonal preconditioner.

Original language	English
Pages (from-to)	2389-2399
Number of pages	11
Journal	Computers and Structures
Volume	82
Issue number	28 SPEC. ISS.
DOIs	https://doi.org/10.1016/j.compstruc.2004.04.011
State	Published - Nov 2004

Bibliographical note

Funding Information:
This study was funded by the US Department of Energy Office of Science under the project “Development of a High Performance Anisotropic Diffusion Equation Solver Using the ACTS Toolkit” (DE-FG02-02ER45961). The first two authors would also like to acknowledge DOE’s support of their attending the ACTS Workshop, organized by Drs. Tony Drummond and Osni Marques, at the Lawrence Berkeley National Laboratory, in September 2002. We would also like to thank Dr. Daniel Gembris, at Institute for Medicine, Jülich Research Center, Jülich, Germany, for providing the diffusion tensor data set.

Keywords

Anisotropic diffusion simulation
Banded-block-diagonal preconditioner
Diffusion tensor magnetic resonance imaging (DT-MRI)
Implicit differential-algebraic solver
Parallel preconditioning techniques
Sparse approximate inverse preconditioner

ASJC Scopus subject areas

Civil and Structural Engineering
Modeling and Simulation
General Materials Science
Mechanical Engineering
Computer Science Applications

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10.1016/j.compstruc.2004.04.011

Cite this

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title = "Parallel simulation of anisotropic diffusion with human brain DT-MRI data",

abstract = "We conduct simulations for the 3D unsteady state anisotropic diffusion process with DT-MRI data in the human brain by discretizing the governing diffusion equation on Cartesian grid and adopting a high performance differential-algebraic equation (DAE) solver, the parallel version of implicit differential-algebraic (IDA) solver, to tackle the resulting large scale system of DAEs. Parallel preconditioning techniques including sparse approximate inverse and banded-block-diagonal preconditioners are used with the GMRES method to accelerate the convergence rate of the iterative solution. We then investigate and compare the efficiency and effectiveness of the two parallel preconditioners. The experimental results of the diffusion simulations on a parallel supercomputer show that the sparse approximate inverse preconditioning strategy, which is robust and efficient with good scalability, gives a much better overall performance than the banded-block-diagonal preconditioner.",

keywords = "Anisotropic diffusion simulation, Banded-block-diagonal preconditioner, Diffusion tensor magnetic resonance imaging (DT-MRI), Implicit differential-algebraic solver, Parallel preconditioning techniques, Sparse approximate inverse preconditioner",

author = "Ning Kang and Jun Zhang and Carlson, {Eric S.}",

note = "Funding Information: This study was funded by the US Department of Energy Office of Science under the project “Development of a High Performance Anisotropic Diffusion Equation Solver Using the ACTS Toolkit” (DE-FG02-02ER45961). The first two authors would also like to acknowledge DOE{\textquoteright}s support of their attending the ACTS Workshop, organized by Drs. Tony Drummond and Osni Marques, at the Lawrence Berkeley National Laboratory, in September 2002. We would also like to thank Dr. Daniel Gembris, at Institute for Medicine, J{\"u}lich Research Center, J{\"u}lich, Germany, for providing the diffusion tensor data set.",

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AU - Kang, Ning

AU - Zhang, Jun

AU - Carlson, Eric S.

N1 - Funding Information: This study was funded by the US Department of Energy Office of Science under the project “Development of a High Performance Anisotropic Diffusion Equation Solver Using the ACTS Toolkit” (DE-FG02-02ER45961). The first two authors would also like to acknowledge DOE’s support of their attending the ACTS Workshop, organized by Drs. Tony Drummond and Osni Marques, at the Lawrence Berkeley National Laboratory, in September 2002. We would also like to thank Dr. Daniel Gembris, at Institute for Medicine, Jülich Research Center, Jülich, Germany, for providing the diffusion tensor data set.

PY - 2004/11

Y1 - 2004/11

N2 - We conduct simulations for the 3D unsteady state anisotropic diffusion process with DT-MRI data in the human brain by discretizing the governing diffusion equation on Cartesian grid and adopting a high performance differential-algebraic equation (DAE) solver, the parallel version of implicit differential-algebraic (IDA) solver, to tackle the resulting large scale system of DAEs. Parallel preconditioning techniques including sparse approximate inverse and banded-block-diagonal preconditioners are used with the GMRES method to accelerate the convergence rate of the iterative solution. We then investigate and compare the efficiency and effectiveness of the two parallel preconditioners. The experimental results of the diffusion simulations on a parallel supercomputer show that the sparse approximate inverse preconditioning strategy, which is robust and efficient with good scalability, gives a much better overall performance than the banded-block-diagonal preconditioner.

AB - We conduct simulations for the 3D unsteady state anisotropic diffusion process with DT-MRI data in the human brain by discretizing the governing diffusion equation on Cartesian grid and adopting a high performance differential-algebraic equation (DAE) solver, the parallel version of implicit differential-algebraic (IDA) solver, to tackle the resulting large scale system of DAEs. Parallel preconditioning techniques including sparse approximate inverse and banded-block-diagonal preconditioners are used with the GMRES method to accelerate the convergence rate of the iterative solution. We then investigate and compare the efficiency and effectiveness of the two parallel preconditioners. The experimental results of the diffusion simulations on a parallel supercomputer show that the sparse approximate inverse preconditioning strategy, which is robust and efficient with good scalability, gives a much better overall performance than the banded-block-diagonal preconditioner.

KW - Anisotropic diffusion simulation

KW - Banded-block-diagonal preconditioner

KW - Diffusion tensor magnetic resonance imaging (DT-MRI)

KW - Implicit differential-algebraic solver

KW - Parallel preconditioning techniques

KW - Sparse approximate inverse preconditioner

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Parallel simulation of anisotropic diffusion with human brain DT-MRI data

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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