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Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs

Hernández, Moisés ; Guerrero, Ginés D ; Cecilia, José M ; García, José M ; Inuggi, Alberto ; Jbabdi, Saad ; Behrens, Timothy E J ; Sotiropoulos, Stamatios N Yacoub, Essa

PloS one, 2013-04, Vol.8 (4), p.e61892-e61892 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs
Autor: Hernández, Moisés ; Guerrero, Ginés D ; Cecilia, José M ; García, José M ; Inuggi, Alberto ; Jbabdi, Saad ; Behrens, Timothy E J ; Sotiropoulos, Stamatios N
Yacoub, Essa
Assuntos: Algorithms ; Artificial intelligence ; Bayesian analysis ; Brain ; Brain - physiology ; Brain - ultrastructure ; Central processing units ; Computational neuroscience ; Computer graphics ; Computer Graphics - statistics & numerical data ; Computer Science ; Computer simulation ; CPUs ; Data processing ; Diffusion ; Diffusion Magnetic Resonance Imaging - statistics & numerical data ; Engineering ; Fiber orientation ; Graphics processing units ; Humans ; Image Interpretation, Computer-Assisted ; Magnetic resonance ; Magnetic resonance imaging ; Male ; Markov Chains ; Markov processes ; Massively parallel processors ; Mathematics ; Medicine ; Microprocessors ; Monte Carlo Method ; Multiprocessing ; Neural networks ; Neuroimaging ; NMR ; Nuclear magnetic resonance ; Parallel processing ; Parameter estimation ; Processors ; Resonance ; Software ; Statistical inference ; Weight reduction
É parte de: PloS one, 2013-04, Vol.8 (4), p.e61892-e61892
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: MH GDG JMC JMG AI SNS. Performed the experiments: MH. Analyzed the data: MH SJ SNS. Contributed reagents/materials/analysis tools: MH TEJB SNS. Wrote the paper: MH JMC JMG SJ TEJB SNS.
Descrição: With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.
Editor: United States: Public Library of Science
Idioma: Inglês

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Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs

Hernández, Moisés ; Guerrero, Ginés D ; Cecilia, José M ; García, José M ; Inuggi, Alberto ; Jbabdi, Saad ; Behrens, Timothy E J ; Sotiropoulos, Stamatios N Yacoub, Essa

United States: Public Library of Science

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