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A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging. / Hu, B.; Glover, P.; Benson, T.
Computational Problem-Solving (ICCP), 2011 International Conference on . IEEE, 2011. p. 431-435.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Hu, B, Glover, P & Benson, T 2011, A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging. in Computational Problem-Solving (ICCP), 2011 International Conference on . IEEE, pp. 431-435, Computational Problem-Solving (ICCP), 2011 International Conference on, Chongqing, China, 21/10/11. https://doi.org/10.1109/ICCPS.2011.6092293

APA

Hu, B., Glover, P., & Benson, T. (2011). A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging. In Computational Problem-Solving (ICCP), 2011 International Conference on (pp. 431-435). IEEE. https://doi.org/10.1109/ICCPS.2011.6092293

Vancouver

Hu B, Glover P, Benson T. A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging. In Computational Problem-Solving (ICCP), 2011 International Conference on . IEEE. 2011. p. 431-435 doi: 10.1109/ICCPS.2011.6092293

Author

Hu, B. ; Glover, P. ; Benson, T. / A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging. Computational Problem-Solving (ICCP), 2011 International Conference on . IEEE, 2011. pp. 431-435

Bibtex

@inproceedings{80620a18437e42dd91b250a0b70ac48e,
title = "A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging",
abstract = "With the recent development of high field MRI scanners, the risk for healthcare staff being exposed to large static magnetic fields (3T to 7T) and rapidly time-varying magnetic field gradients is greatly increased. A better understanding of the interaction mechanisms and the bio-effects associated with MRI environment would allow sensible and workable exposure limits to be set for staff, patients and volunteers. This paper presents a novel approach in modeling hazardous electric field levels induced in a human body under continuous movements within a strong magnetic field environment. The derived algorithm is able to accurately model both translational motion and rotating body movements. Since this algorithm is based on the quasi-static Finite-Difference approximation, the computational space for modeling a human body can then be divided into a large number of cubic cells. Every cell in the model is very suitable for parallelization and hardware acceleration using General Purpose Graphical Processing Units (GPGPU). After adopting several optimization techniques, a speedup of around 40 times is achieved by adopting GPGPU for modeling torso movements around 8 million cells compared with a CPU implementation.",
author = "B. Hu and P. Glover and T. Benson",
year = "2011",
doi = "10.1109/ICCPS.2011.6092293",
language = "English",
isbn = "978-1-4577-0602-8",
pages = "431--435",
booktitle = "Computational Problem-Solving (ICCP), 2011 International Conference on",
publisher = "IEEE",
note = "Computational Problem-Solving (ICCP), 2011 International Conference on ; Conference date: 21-10-2011 Through 23-10-2011",

}

RIS

TY - GEN

T1 - A GPU accelerated modeling of bio-effects associated with magnetic resonance imaging

AU - Hu, B.

AU - Glover, P.

AU - Benson, T.

PY - 2011

Y1 - 2011

N2 - With the recent development of high field MRI scanners, the risk for healthcare staff being exposed to large static magnetic fields (3T to 7T) and rapidly time-varying magnetic field gradients is greatly increased. A better understanding of the interaction mechanisms and the bio-effects associated with MRI environment would allow sensible and workable exposure limits to be set for staff, patients and volunteers. This paper presents a novel approach in modeling hazardous electric field levels induced in a human body under continuous movements within a strong magnetic field environment. The derived algorithm is able to accurately model both translational motion and rotating body movements. Since this algorithm is based on the quasi-static Finite-Difference approximation, the computational space for modeling a human body can then be divided into a large number of cubic cells. Every cell in the model is very suitable for parallelization and hardware acceleration using General Purpose Graphical Processing Units (GPGPU). After adopting several optimization techniques, a speedup of around 40 times is achieved by adopting GPGPU for modeling torso movements around 8 million cells compared with a CPU implementation.

AB - With the recent development of high field MRI scanners, the risk for healthcare staff being exposed to large static magnetic fields (3T to 7T) and rapidly time-varying magnetic field gradients is greatly increased. A better understanding of the interaction mechanisms and the bio-effects associated with MRI environment would allow sensible and workable exposure limits to be set for staff, patients and volunteers. This paper presents a novel approach in modeling hazardous electric field levels induced in a human body under continuous movements within a strong magnetic field environment. The derived algorithm is able to accurately model both translational motion and rotating body movements. Since this algorithm is based on the quasi-static Finite-Difference approximation, the computational space for modeling a human body can then be divided into a large number of cubic cells. Every cell in the model is very suitable for parallelization and hardware acceleration using General Purpose Graphical Processing Units (GPGPU). After adopting several optimization techniques, a speedup of around 40 times is achieved by adopting GPGPU for modeling torso movements around 8 million cells compared with a CPU implementation.

U2 - 10.1109/ICCPS.2011.6092293

DO - 10.1109/ICCPS.2011.6092293

M3 - Conference contribution/Paper

SN - 978-1-4577-0602-8

SP - 431

EP - 435

BT - Computational Problem-Solving (ICCP), 2011 International Conference on

PB - IEEE

T2 - Computational Problem-Solving (ICCP), 2011 International Conference on

Y2 - 21 October 2011 through 23 October 2011

ER -