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Fast GPU based adaptive filtering of 4D echocardiography
Örebro University, School of Science and Technology. Centre of Biomedical Engineering Research (MTFC), Örebro University Hospital, Örebro, Sweden. (Centre for Modeling and Simulation, Campus Alfred Nobel, Karlskoga, sweden)
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Clinical Physiology, Örebro University Hospital, Örebro, Sweden.
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Medical Physics and Centre of Biomedical Engineering Research (MTFC), Örebro University Hospital, Örebro, Sweden.ORCID iD: 0000-0002-8351-3367
2012 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 31, no 6, p. 1165-1172, article id 6099625Article in journal (Refereed) Published
Abstract [en]

Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2012. Vol. 31, no 6, p. 1165-1172, article id 6099625
Keywords [en]
Echocardiography, high performance computing, image denoising, image enhancement, parallel computing
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-24981DOI: 10.1109/TMI.2011.2179308ISI: 000304911300001PubMedID: 22167599Scopus ID: 2-s2.0-84861840831OAI: oai:DiVA.org:oru-24981DiVA, id: diva2:546630
Note

Funding agencies:

EU 

Research Committe of Örebro County Council  

Available from: 2012-08-24 Created: 2012-08-23 Last updated: 2018-09-12Bibliographically approved

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Broxvall, MathiasEmilsson, KentThunberg, Per

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