119 research outputs found

    MRI Data Processing Acceleration on GPU

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    Tato bakalářská práce byla vypracována v průběhu studijního pobytu na Universita della Svizzera italiana ve Švýcarsku. Identifikace trajektorií neuronových vláken uvnitř lidského mozku má velký význam v mnoha lékařských aplikacích, jako neurologická diagnostika, neuro-navigace, léčba epilepsie, chirurgické operace a tak dále. Za použití dat z MRI, metod postavených na Markovských řetězích a Monte Carlu mohou být možné trajektorie vypočítany a ty nejpravděpodobnější zobrazeny. Tyto informace o trajektoriích mohou sloužit jako vstup pro pokročilé metody lékařské diagnotiky a léčby. Vzhledem k obrovskému množství dat a velkého počtu iterací toto může být časově náročný proces. Za účely, jako jsou statistická analýza a/nebo porovnávání několika datových sad a/nebo pacientů, požadavky na výpočetní čas jsou enormní. Rychlejší diagnóza může také přinést nasazení léčby dříve. Nyní existuje jen velmi málo implementací softwaru pro neurální traktografii. Implementací softwaru pro pravděpodobnostní neurální traktografii je ještě méně. Nynější implementace, provádějící všechny operace postupně na CPU, jsou značně pomalé. Účelem této práce je poskytnout efektivní implementaci, která vvyužíva GPU. Za účelem implementace na GPU, je poskytnuto porovnaní technologíí CUDA a OpenCL.This BSc Thesis was performed during a study stay at the Universita della Svizzera italiana, Swiss. The identification of trajectories of neuron fibres within the human brain is of great importance in many medical applications as the neural diagnostics, neuronavigation, treatment of epilepsy, surgical removal of tumors and etc. By using diffusion MRI-data as input, and by employing Monte-Carlo like methods, possible trajectories are generated and the most likely ones can be visualized. These can serve as input for advanced medical diagnosis and treatments. Due to the huge amount of data to be analyzed and many iterations, this is a time consuming process. For the purposes such as statistical analysis and comparsion over several datasets or several patients, computational time requirements are enourmous. Faster diagnosis can improve routine throughput and provide earlier treatment of illness. At this time, there exists only a very few implementations of neural tractography sof tware. For probabilistic neural tractography is the list of software even thiner. Today's implementations using standard serial CPU execution suffer from high time consumption. The goal is to provide an efficient implementation which makes use of GPGPUs and exploits parallelism in the method. For the GPU implementation, a comparsion of CUDA and OpenCL technologies will be provided, using the more suitable one.

    Bayesian uncertainty quantification in linear models for diffusion MRI

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    Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.Comment: Added results from a group analysis and a comparison with residual bootstra

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

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    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

    Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity

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    The ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results. Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds. We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.88 mm (left) and 2.12 mm (right) of the average midpoint and 3.98 mm (left) and 5.41 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTC reconstructed. The method was reproducible, with no significant cd-Vim location differences in a separate test-retest cohort. The superior cerebellar peduncle was identified as a potential source of artificial variance. This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based coordinate targeting fails to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; these findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor

    Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning

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    Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs. Our method is motivated by the key observation that each fiber segment is affected by a limited spatial neighborhood. In other words, a fiber segment is influenced only by the fiber segments that are (or can potentially be) connected to its two ends and also by the diffusion-weighted signal in its proximity. This observation makes it possible to parallelize the Markov chain Monte Carlo (MCMC) algorithm used in the global tractography algorithm so that concurrent updating of independent fiber segments can be carried out. Experiments show that the proposed algorithm can significantly speed up global tractography, while at the same time maintain or even improve tractography performance
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