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MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations

Xanthis, Christos LU ; Venetis, Ioannis E.; Chalkias, A. V. and Aletras, Anthony LU (2014) In IEEE Transactions on Medical Imaging 33(3). p.607-617
Abstract
A new step-by-step comprehensive MR physics simulator (MRISIMUL) of the Bloch equations is presented. The aim was to develop a magnetic resonance imaging (MRI) simulator that makes no assumptions with respect to the underlying pulse sequence and also allows for complex large-scale analysis on a single computer without requiring simplifications of the MRI model. We hypothesized that such a simulation platform could be developed with parallel acceleration of the executable core within the graphic processing unit (GPU) environment. MRISIMUL integrates realistic aspects of the MRI experiment from signal generation to image formation and solves the entire complex problem for densely spaced isochromats and for a densely spaced time axis. The... (More)
A new step-by-step comprehensive MR physics simulator (MRISIMUL) of the Bloch equations is presented. The aim was to develop a magnetic resonance imaging (MRI) simulator that makes no assumptions with respect to the underlying pulse sequence and also allows for complex large-scale analysis on a single computer without requiring simplifications of the MRI model. We hypothesized that such a simulation platform could be developed with parallel acceleration of the executable core within the graphic processing unit (GPU) environment. MRISIMUL integrates realistic aspects of the MRI experiment from signal generation to image formation and solves the entire complex problem for densely spaced isochromats and for a densely spaced time axis. The simulation platform was developed in MATLAB whereas the computationally demanding core services were developed in CUDA-C. The MRISIMUL simulator imaged three different computer models: a user-defined phantom, a human brain model and a human heart model. The high computational power of GPU-based simulations was compared against other computer configurations. A speedup of about 228 times was achieved when compared to serially executed C-code on the CPU whereas a speedup between 31 to 115 times was achieved when compared to the OpenMP parallel executed C-code on the CPU, depending on the number of threads used in multithreading (2-8 threads). The high performance of MRISIMUL allows its application in large-scale analysis and can bring the computational power of a supercomputer or a large computer cluster to a single GPU personal computer. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Graphic processing unit (GPU), magnetic resonance imaging (MRI), parallel, simulator
in
IEEE Transactions on Medical Imaging
volume
33
issue
3
pages
607 - 617
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000332599500002
  • scopus:84896124899
ISSN
1558-254X
DOI
10.1109/TMI.2013.2292119
language
English
LU publication?
yes
id
e8b13cf9-bd86-42d5-baf3-36c90903a54a (old id 4414315)
date added to LUP
2014-05-05 07:16:02
date last changed
2017-10-22 03:06:48
@article{e8b13cf9-bd86-42d5-baf3-36c90903a54a,
  abstract     = {A new step-by-step comprehensive MR physics simulator (MRISIMUL) of the Bloch equations is presented. The aim was to develop a magnetic resonance imaging (MRI) simulator that makes no assumptions with respect to the underlying pulse sequence and also allows for complex large-scale analysis on a single computer without requiring simplifications of the MRI model. We hypothesized that such a simulation platform could be developed with parallel acceleration of the executable core within the graphic processing unit (GPU) environment. MRISIMUL integrates realistic aspects of the MRI experiment from signal generation to image formation and solves the entire complex problem for densely spaced isochromats and for a densely spaced time axis. The simulation platform was developed in MATLAB whereas the computationally demanding core services were developed in CUDA-C. The MRISIMUL simulator imaged three different computer models: a user-defined phantom, a human brain model and a human heart model. The high computational power of GPU-based simulations was compared against other computer configurations. A speedup of about 228 times was achieved when compared to serially executed C-code on the CPU whereas a speedup between 31 to 115 times was achieved when compared to the OpenMP parallel executed C-code on the CPU, depending on the number of threads used in multithreading (2-8 threads). The high performance of MRISIMUL allows its application in large-scale analysis and can bring the computational power of a supercomputer or a large computer cluster to a single GPU personal computer.},
  author       = {Xanthis, Christos and Venetis, Ioannis E. and Chalkias, A. V. and Aletras, Anthony},
  issn         = {1558-254X},
  keyword      = {Graphic processing unit (GPU),magnetic resonance imaging (MRI),parallel,simulator},
  language     = {eng},
  number       = {3},
  pages        = {607--617},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Medical Imaging},
  title        = {MRISIMUL: A GPU-Based Parallel Approach to MRI Simulations},
  url          = {http://dx.doi.org/10.1109/TMI.2013.2292119},
  volume       = {33},
  year         = {2014},
}