title: Cloud GPU-based simulations for SQUAREMR.
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 T1quantification (T1mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1mapping 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 T1mapping; however, execution times may exceed 30min 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 T1estimates 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 28s using the 16-node cluster, without compromising the T1estimates by more than 10ms. 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.
authors.affiliation: Laboratory of Computing and Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: George
authors.initials: G
authors.lastname: Kantasis
authors.affiliation: Lund Cardiac MR Group, Department of Clinical Physiology and Nuclear Medicine, Lund University, Skåne University Hospital in Lund, Lund, Sweden; Laboratory of Computing and Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Christos G
authors.initials: CG
authors.lastname: Xanthis
authors.affiliation: Laboratory of Computing and Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
authors.forename: Kostas
authors.initials: K
authors.lastname: Haris
authors.affiliation: Lund Cardiac MR Group, Department of Clinical Physiology and Nuclear Medicine, Lund University, Skåne University Hospital in Lund, Lund, Sweden; Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
authors.forename: Einar
authors.initials: E
authors.lastname: Heiberg
authors.affiliation: Laboratory of Computing and Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece; Lund Cardiac MR Group, Department of Clinical Physiology and Nuclear Medicine, Lund University, Skåne University Hospital in Lund, Lund, Sweden. Electronic address: aletras@auth.gr.
authors.forename: Anthony H
authors.initials: AH
authors.lastname: Aletras
keywords: Cloud; Magnetic Resonance Imaging; Relaxometry; Simulations