| DOI | Resolve DOI: https://doi.org/10.1016/j.mri.2014.08.019 |
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| Author | Search for: Bergen, Robert V.; Search for: Lin, Hung-Yu1; Search for: Alexander, Murray E.; Search for: Bidinosti, Christopher P. |
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| Name affiliation | - National Research Council of Canada. Medical Devices
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| Format | Text, Article |
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| Subject | algorithm; aorta flow; cardiovascular magnetic resonance; central processing unit; contrast radiography; graphics processing unit; image acquisition; image analysis; image processing; information processing; magnitude segmentation; mathematical computing; normal distribution; phase transition; segmentation algorithm; waveform |
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| Abstract | The increasing size and number of data sets of large four dimensional (three spatial, one temporal) magnetic resonance (MR) cardiac images necessitates efficient segmentation algorithms. Analysis of phase-contrast MR images yields cardiac flow information which can be manipulated to produce accurate segmentations of the aorta. Phase contrast segmentation algorithms are proposed that use simple mean-based calculations and least mean squared curve fitting techniques. The initial segmentations are generated on a multi-threaded central processing unit (CPU) in 10. seconds or less, though the computational simplicity of the algorithms results in a loss of accuracy. A more complex graphics processing unit (GPU)-based algorithm fits flow data to Gaussian waveforms, and produces an initial segmentation in 0.5. seconds. Level sets are then applied to a magnitude image, where the initial conditions are given by the previous CPU and GPU algorithms. A comparison of results shows that the GPU algorithm appears to produce the most accurate segmentation. |
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| Publication date | 2015-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21275718 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | f87ae55b-8664-493f-9ac6-3dc9e73948e0 |
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| Record created | 2015-07-14 |
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| Record modified | 2020-04-22 |
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