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Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU
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Metadata
Document Title
Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU
Author
Techavipoo U, Worasawate D, Boonleelakul W, Keinprasit R, Sunpetchniyom T, Sugino N, Thajchayapong P
Name from Authors Collection
Affiliations
National Science & Technology Development Agency - Thailand; National Electronics & Computer Technology Center (NECTEC); Kasetsart University; Tokyo Institute of Technology
Type
Article
Source Title
SENSORS
Year
2016
Volume
16
Issue
12
Page
-
Open Access
gold, Green Submitted, Green Published
Publisher
MDPI
DOI
10.3390/s16121986
Format
Abstract
An ultrasound image is reconstructed from echo signals received by array elements of a transducer. The time of flight of the echo depends on the distance between the focus to the array elements. The received echo signals have to be delayed to make their wave fronts and phase coherent before summing the signals. In digital beamforming, the delays are not always located at the sampled points. Generally, the values of the delayed signals are estimated by the values of the nearest samples. This method is fast and easy, however inaccurate. There are other methods available for increasing the accuracy of the delayed signals and, consequently, the quality of the beamformed signals; for example, the in-phase (I)/quadrature (Q) interpolation, which is more time consuming but provides more accurate values than the nearest samples. This paper compares the signals after dynamic receive beamforming, in which the echo signals are delayed using two methods, the nearest sample method and the I/Q interpolation method. The comparisons of the visual qualities of the reconstructed images and the qualities of the beamformed signals are reported. Moreover, the computational speeds of these methods are also optimized by reorganizing the data processing flow and by applying the graphics processing unit (GPU). The use of single and double precision floating-point formats of the intermediate data is also considered. The speeds with and without these optimizations are also compared.
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Funding Sponsor
Thailand Toray Science Foundation (TTSF) research grant; Thailand Research Fund (TRF) grant; Thailand Advanced Institute of Science and Technology-Tokyo Institute of Technology-Kasetsart University (TAIST-Tokyo Tech-KU)
License
CC BY
Rights
Authors
Publication Source
WOS