Δημοσίευση

MRISIMUL: a GPU-based parallel approach to MRI simulations.

ΤίτλοςMRISIMUL: a GPU-based parallel approach to MRI simulations.
Publication TypeJournal Article
Year of Publication2014
AuthorsXanthis, C. G., Venetis I. E., Chalkias A. V., & Aletras A. H.
JournalIEEE Trans Med Imaging
Volume33
Issue3
Pagination607-17
Date Published2014 Mar
ISSN1558-254X
Λέξεις κλειδιάBrain, Computer Graphics, Computer Simulation, Female, Heart, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Phantoms, Imaging, Signal Processing, Computer-Assisted
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.

DOI10.1109/TMI.2013.2292119
Alternate JournalIEEE Trans Med Imaging
PubMed ID24595337

Επικοινωνία

Τμήμα Ιατρικής, Πανεπιστημιούπολη ΑΠΘ, T.K. 54124, Θεσσαλονίκη
 

Συνδεθείτε

Το τμήμα Ιατρικής στα κοινωνικά δίκτυα.
Ακολουθήστε μας ή συνδεθείτε μαζί μας.