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Fast GPU based adap...
Fast GPU based adaptive filtering of 4D echocardiography
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- Broxvall, Mathias, 1976- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre of Biomedical Engineering Research (MTFC), Örebro University Hospital, Örebro, Sweden,Centre for Modeling and Simulation, Campus Alfred Nobel, Karlskoga, sweden
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- Emilsson, Kent, 1963- (författare)
- Örebro universitet,Institutionen för hälsovetenskap och medicin,Department of Clinical Physiology, Örebro University Hospital, Örebro, Sweden
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- Thunberg, Per, 1968- (författare)
- Örebro universitet,Institutionen för hälsovetenskap och medicin,Department of Medical Physics and Centre of Biomedical Engineering Research (MTFC), Örebro University Hospital, Örebro, Sweden
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(creator_code:org_t)
- Piscataway, USA : Institute of Electrical and Electronics Engineers (IEEE), 2012
- 2012
- Engelska.
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Ingår i: IEEE Transactions on Medical Imaging. - Piscataway, USA : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 31:6, s. 1165-1172
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Echocardiography
- high performance computing
- image denoising
- image enhancement
- parallel computing
- Datalogi
- Computer and Systems Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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