GPU-based computational modeling of magnetic resonance imaging of vascular structures

Abstract : Magnetic resonance imaging (MRI) is one of the most important diagnostic tools in modern medicine. Since it is a high-cost and highly-complex imaging modality, computational models are frequently built to enhance its understanding as well as to support further development. However, such models often have to be simplified to complete simulations in a reasonable time. Thus, the simulations with high spatial/temporal resolutions, with any motion consideration (like blood flow) and/or with 3D objects usually call for using parallel computing environments. In this paper, we propose to use graphics processing units (GPUs) for fast simulations of MRI of vascular structures. We apply a CUDA environment which supports general purpose computation on GPU (GPGPU). The data decomposition strategy is applied and thus the parts of each virtual object are spread over the GPU cores. The GPU cores are responsible for calculating the influence of blood flow behavior and MRI events after successive time steps. In the proposed approach, different data layouts, memory access patterns, and other memory improvements are applied to efficiently exploit GPU resources. Computational performance is thoroughly validated for various vascular structures and different NVIDIA GPUs. Results show that MRI simulations can be accelerated significantly thanks to GPGPU. The proposed GPU-based approach may be easily adopted in the modeling of other flow related phenomena like perfusion, diffusion or transport of contrast agents.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01861555
Contributor : Laurent Jonchère <>
Submitted on : Friday, August 24, 2018 - 3:20:48 PM
Last modification on : Friday, June 21, 2019 - 10:19:32 AM

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Krzysztof Jurczuk, Marek Kretowski, Johanne Bezy-Wendling. GPU-based computational modeling of magnetic resonance imaging of vascular structures. International Journal of High Performance Computing Applications, SAGE Publications, 2018, 32 (4), pp.496-511. ⟨10.1177/1094342016677586⟩. ⟨hal-01861555⟩

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