Cloud GPU-based simulations for SQUAREMR
Kantasis, George ; Xanthis, Christos G. LU ; Haris, Kostas LU ; Heiberg, Einar LU and Aletras, Anthony H. LU
(2017)
In Journal of Magnetic Resonance
274.
p.80-88
- Abstract
Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T1 quantification (T1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of... (More)
Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T1 quantification (T1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T1 mapping; however, execution times may exceed 30 min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T1 estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28 s using the 16-node cluster, without compromising the T1 estimates by more than 10 ms. The developed cloud-based cluster and optimization of the parameter set reduced the execution time of the simulations involved in constructing the SQUAREMR multi-parametric database thus bringing SQUAREMR's applicability within time frames that would be likely acceptable in the clinic.
(Less)
- author
- Kantasis, George
; Xanthis, Christos G.
LU
; Haris, Kostas
LU
; Heiberg, Einar
LU
and Aletras, Anthony H.
LU
- organization
- publishing date
- 2017-01-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cloud, Magnetic Resonance Imaging, Relaxometry, Simulations
- in
- Journal of Magnetic Resonance
- volume
- 274
- pages
- 9 pages
- publisher
- Academic Press
- external identifiers
-
- pmid:27889652
- wos:000393446000010
- scopus:84997235060
- ISSN
- 1090-7807
- DOI
- 10.1016/j.jmr.2016.11.007
- language
- English
- LU publication?
- yes
- id
- 988e0e64-5e78-4865-849b-b6e329a44481
- date added to LUP
- 2017-03-17 11:14:36
- date last changed
- 2025-01-07 09:58:09
@article{988e0e64-5e78-4865-849b-b6e329a44481,
abstract = {{<p>Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T<sub>1</sub> quantification (T<sub>1</sub> mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T<sub>1</sub> mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T<sub>1</sub> mapping; however, execution times may exceed 30 min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T<sub>1</sub> estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28 s using the 16-node cluster, without compromising the T<sub>1</sub> estimates by more than 10 ms. The developed cloud-based cluster and optimization of the parameter set reduced the execution time of the simulations involved in constructing the SQUAREMR multi-parametric database thus bringing SQUAREMR's applicability within time frames that would be likely acceptable in the clinic.</p>}},
author = {{Kantasis, George and Xanthis, Christos G. and Haris, Kostas and Heiberg, Einar and Aletras, Anthony H.}},
issn = {{1090-7807}},
keywords = {{Cloud; Magnetic Resonance Imaging; Relaxometry; Simulations}},
language = {{eng}},
month = {{01}},
pages = {{80--88}},
publisher = {{Academic Press}},
series = {{Journal of Magnetic Resonance}},
title = {{Cloud GPU-based simulations for SQUAREMR}},
url = {{http://dx.doi.org/10.1016/j.jmr.2016.11.007}},
doi = {{10.1016/j.jmr.2016.11.007}},
volume = {{274}},
year = {{2017}},
}