4D MR phase and magnitude segmentations with GPU parallel computing

Bergen, Robert V.; Lin, Hung‐Yu; Alexander, Murray E.; Bidinosti, Christopher

doi:10.1016/j.mri.2014.08.019

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…The increasing size of the 4D data sets by magnetic resonance imaging (MRI) requires effective cardiac segmentation algorithms. A deep study in [55] recall that the segmentation phases are generated in a multithreaded CPU in 10 seconds or less.…”

Section: Hybrid Multicore Applied To Dynamic Fluidmentioning

confidence: 99%

Moving Towards a 5d Cardiac Model

Sakly¹,

Mahmoudi²,

Akil³

et al. 2019

J Flow Vis Image Proc

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The medical diagnosis process requires several steps to identify the types of cardiac pathologies. The segmentation step is used to determine the measurements for cardiac abnormalities on the short axis of the 4D acquired in MRI, but this phase remains limited on the blood flow sequences. The MRI modality allow to the experts to quantify the stenosis and the regurgitation of aortic blood flow. The parameters extracted from the flow sequences, after segmentation, make it possible to identify the valvular pathologies, but they are not sufficient to complete the medical prognosis as well as the lack of precision of these measurements. In this paper, we propose to make a coupling between the 4D cardiac cuts with their study of blood flow through the technique of registration and reconstruction. The interest of the purpose of the blood flow as fifth dimension is to improve the accuracy of the cardiac parameters and the extracting of measurements for valvulopathies in the process of assistance to the decision for the experts An example was introduced in this context through the development of cloud services for patient-specific simulations of blood flows through aortic valves as well as an OsiriX software for 5D visualization that combines a 4D sequence and the functional flow dimension. In this framework, we proposed a processing chain to lead towards a 5D solution. Another problem is raised is the choice of the appropriate architecture to solve the problem of hybrid parallelization for the processing of these cardiac images. To test the constraints of time of the concept of 5D, we need a GPU graphics processor acquired in MRI, as well as a CPU processor to perform the complexity of calculation and the operations applied to the algorithms of image processing.

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Section: Hybrid Multicore Applied To Dynamic Fluidmentioning

confidence: 99%

Moving Towards a 5d Cardiac Model

Sakly¹,

Mahmoudi²,

Akil³

et al. 2019

J Flow Vis Image Proc

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“…Bergen et al. [BLAB15] analysed each voxel in

V_{{x, y, z}}

along the temporal dimension. When plotted, one curve should resemble a typical flow curve, whereas outside voxels produce noise curves (Figure ).…”

Section: Vessel Segmentationmentioning

confidence: 99%

“…The blue curve in (a) resembles a typical healthy volunteer's flow curve and is to be identified by a curve fitting procedure. Images based on Bergen et al[BLAB15].…”

mentioning

confidence: 99%

A Survey of Cardiac 4D PC‐MRI Data Processing

Köhler

Born

Pelt

et al. 2016

Computer Graphics Forum

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Cardiac four-dimensional phase-contrast magnetic resonance imaging (4D PC-MRI) acquisitions have gained increasing clinical interest in recent years. They allow to non-invasively obtain extensive information about patient-specific hemodynamics, and thus have a great potential to improve the diagnosis, prognosis and therapy planning of cardiovascular diseases. A dataset contains time-resolved, three-dimensional blood flow directions and strengths, making comprehensive qualitative and quantitative data analysis possible. Quantitative measures, such as stroke volumes, help to assess the cardiac function and to monitor disease progression. Qualitative analysis allows to investigate abnormal flow characteristics, such as vortices, which are correlated to different pathologies. Processing the data comprises complex image processing methods, as well as flow analysis and visualization. In this work, we mainly focus on the aorta. We provide an overview of data measurement and pre-processing, as well as current visualization and quantification methods. This allows other researchers to quickly catch up with the topic and take on new challenges to further investigate the potential of 4D PC-MRI data.

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“…Ruiz et al developed parallel expectation maximization with linear discriminant analysis (EMLDA) and K-means with GPGPU to segment different image components thus to reduce the computational analysis of histopathological images of neuroblastoma [40]. Bergen et al developed a GPU-enabled approach to segmentation of magnitude images, which enabled a fast initial segmentation (20 times faster comparing to the CPU-based approach) and provided a segmentation with much higher accuracy [41]. Using a Fermi GPU card, Kurihara et al developed an image registration algorithm based on mutual information [42].…”

Section: Neuroimaging With Graphics Processing Unitsmentioning

confidence: 99%

Brain big data processing with massively parallel computing technology: challenges and opportunities

Chen

Cai

et al. 2016

Softw Pract Exp

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Summary Brain data processing has been embracing the big data era driven by the rapid advances of neuroscience as well as the experimental techniques for recording neuronal activities. Processing of massive brain data has become a constant in neuroscience research and practice, which is vital in revealing the hidden information to better understand the brain functions and malfunctions. Brain data are routinely non‐linear and non‐stationary in nature, and existing algorithms and approaches to neural data processing are generally complicated in order to characterize the non‐linearity and non‐stationarity. Brain big data processing has pressing needs for appropriate computing technologies to address three grand challenges: (1) efficiency, (2) scalability and (3) reliability. Recent advances of computing technologies are making non‐linear methods viable in sophisticated applications of massive brain data processing. General‐purpose Computing on the Graphics Processing Unit (GPGPU) technology fosters an ideal environment for this purpose, which benefits from the tremendous computing power of modern graphics processing units in massively parallel architecture that is frequently an order of magnitude larger than the modern multi‐core CPUs. This article first recaps significant speed‐ups of existing algorithms aided by GPGPU in neuroimaging and processing electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and etc. The article then demonstrates a series of successful approaches to processing EEG data in various dimensions and scales in a massively parallel manner: (1) decomposition: a massively parallel Ensemble Local Mean Decomposition (ELMD) algorithm aided by GPGPU can decompose EEG series, which forms the basis of further time‐frequency transformation, in real‐time without sacrificing the precision of processing; (2) synchronization measurement: a parallelized Nonlinear Interdependence (NLI) method for global synchronization measurement of multivariate EEG with speed‐up of more than 1000 times, and it was successful in localization of epileptic focus; and (3) dimensionality reduction: a large‐scale Parallel Factor Analysis which excels in run‐time performance and scales far better by hundreds of times than conventional approach does, and it supports fast factorization of EEG with more than 1000 channels. Through these practices, the massively parallel computing technology manifests great potentials in addressing the grand challenges of brain big data processing. Copyright © 2016 John Wiley & Sons, Ltd.

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4D MR phase and magnitude segmentations with GPU parallel computing

Cited by 7 publications

References 43 publications

Moving Towards a 5d Cardiac Model

Moving Towards a 5d Cardiac Model

A Survey of Cardiac 4D PC‐MRI Data Processing

Brain big data processing with massively parallel computing technology: challenges and opportunities

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