GPU-based reconstruction and display for 4D ultrasound data

Elnokrashy, Ahmed F.; Elmalky, Ahmed A.; Hosny, Tamer; Ellah, Marwan Abd; Megawer, Alaa; Elsebai, Abobakr; Youssef, Abou-Bakr M.; Kadah, Yasser M.

doi:10.1109/ultsym.2009.5441645

Cited by 14 publications

(7 citation statements)

References 2 publications

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“…Fortunately, modern GPUs can concurrently perform calculations and transfer data. Elnokrashy et al (2009) described a lowcost system for reconstruction and display of 4D ultrasound data. Similar frameworks were proposed by Kim et al (2010) and Brattain and Howe (2011).…”

Section: Ultrasoundmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

351

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Section: Ultrasoundmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

351

198

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“…[BJE*11] shows how the Voreen volume rendering engine can be used to visualize real‐time 4D ultrasound data and Elnokrashy et al . [EEH*09] present the basic pipeline setup for GPU‐based ultrasound rendering, but neither work discusses the important issue of filtering. Bronstad et al .…”

Section: Related Workmentioning

confidence: 99%

Output‐Sensitive Filtering of Streaming Volume Data

Šoltészová

Birkeland

Stoppel

et al. 2016

Computer Graphics Forum

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Real‐time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre‐processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high‐quality filtering operations in such scenarios, we propose an output‐sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on‐the‐fly processing.

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“…Volume rendering is considered one of the most important techniques that has been used for investigating the internal structures of volumetric data acquired by different imaging modalities such as Ultrasound and Magnetic Resonance Imag ing (MRI) [1], [2]. It employs various shading strategies and transfer functions to highlight and reveal the objects of interest inside a three-dimensional volume [3].…”

Section: Introductionmentioning

confidence: 99%

MATLAB-based fourier volume rendering framework

Abdellah

Eldeib

Sharawi

2014

2014 Cairo International Biomedical Engineering Conference (CIBEC)

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Volume rendering plays a significant role in medical imaging. It allows exploring the internal structures of volu metric data acquired by the different imaging modalities. This exploration allows accurate diagnosis and consequently effective treatment. Various volume rendering techniques exists. However, compared to other techniques, Fourier volume rendering has gained a wide acceptance by the radiologists for several reasons. This technique generates attenuation renderings similar to x ray radiographs that are well interpreted by the physicians. Additionally, it works with time complexity of O(N 2 ZogN), and thus, it delivers interactive frame rates for large scale medical volumes in comparison with spatial-domain rendering techniques. The complexity associated with developing a basic rendering pipeline for this technique hinders medical imaging scientists from focusing their research on investigating new algorithms for improving the reconstruction quality of the resulting digital radiographs. In this paper, we present a flexible, extensible and semi-interactive high-level MATLAB-based framework for the Fourier volume rendering pipeline.

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GPU-based reconstruction and display for 4D ultrasound data

Cited by 14 publications

References 2 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Output‐Sensitive Filtering of Streaming Volume Data

MATLAB-based fourier volume rendering framework

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