Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs – ScienceOpen
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      Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs

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          Abstract

          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.

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          Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI.

          Parallel imaging in the form of multiband radiofrequency excitation, together with reduced k-space coverage in the phase-encode direction, was applied to human gradient echo functional MRI at 7 T for increased volumetric coverage and concurrent high spatial and temporal resolution. Echo planar imaging with simultaneous acquisition of four coronal slices separated by 44mm and simultaneous 4-fold phase-encoding undersampling, resulting in 16-fold acceleration and up to 16-fold maximal aliasing, was investigated. Task/stimulus-induced signal changes and temporal signal behavior under basal conditions were comparable for multiband and standard single-band excitation and longer pulse repetition times. Robust, whole-brain functional mapping at 7 T, with 2 x 2 x 2mm(3) (pulse repetition time 1.25 sec) and 1 x 1 x 2mm(3) (pulse repetition time 1.5 sec) resolutions, covering fields of view of 256 x 256 x 176 mm(3) and 192 x 172 x 176 mm(3), respectively, was demonstrated with current gradient performance. (c) 2010 Wiley-Liss, Inc.
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            Estimation of the effective self-diffusion tensor from the NMR spin echo.

            The diagonal and off-diagonal elements of the effective self-diffusion tensor, Deff, are related to the echo intensity in an NMR spin-echo experiment. This relationship is used to design experiments from which Deff is estimated. This estimate is validated using isotropic and anisotropic media, i.e., water and skeletal muscle. It is shown that significant errors are made in diffusion NMR spectroscopy and imaging of anisotropic skeletal muscle when off-diagonal elements of Deff are ignored, most notably the loss of information needed to determine fiber orientation. Estimation of Deff provides the theoretical basis for a new MRI modality, diffusion tensor imaging, which provides information about tissue microstructure and its physiologic state not contained in scalar quantities such as T1, T2, proton density, or the scalar apparent diffusion constant.
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              Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.

              In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion MR data for tractography. The non-monoexponential decay, commonly observed in experimental data, is shown to induce overfitting in the distribution of fiber orientations when not considered in the model. Extra fiber orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous gamma distribution of diffusivities, which significantly improves the fitting and reduces the overfitting. Using in vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-monoexponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fiber orientations in white matter and near the cortex. Copyright © 2012 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                29 April 2013
                3 May 2013
                : 8
                : 4
                : e61892
                Affiliations
                [1 ]Department of Computer Science, University of Murcia, Murcia, Spain
                [2 ]Department of Computer Science, Catholic University of Murcia, Murcia, Spain
                [3 ]Basque Center on Cognition, Brain and Language, San Sebastian, Spain
                [4 ]Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
                University of Minnesota, United States of America
                Author notes

                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.

                Article
                PONE-D-12-40109
                10.1371/journal.pone.0061892
                3643787
                23658616
                f3990ea9-170e-4a7e-9a49-b037750c99f9
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 December 2012
                : 14 March 2013
                Page count
                Pages: 13
                Funding
                This research was supported by Fundación Séneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grant 15290/PI/2010, and by the Spanish Ministerio de Educación y Ciencia and European Commission FEDER (Fonds Européen de Développement Régional) under grants TIN2009-14475-C04 and TIN2012-31345. Funding also comes from the Human Connectome Project (1U54MH091657-01) from the 16 National Institutes of Health Institutes and Centers that support the NIH Blueprint for Neuroscience Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer Science
                Algorithms
                Computer Architecture
                Computer Hardware
                Software Engineering
                Software Design
                Engineering
                Bioengineering
                Biomedical Engineering
                Mathematics
                Probability Theory
                Bayes Theorem
                Markov Model
                Probability Distribution
                Medicine
                Anatomy and Physiology
                Neurological System
                Neural Pathways
                Neuroanatomy
                Radiology
                Diagnostic Radiology
                Magnetic Resonance Imaging

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