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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Transactions on...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control
Article . 2018 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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Microbeamforming With Error Compensation

Authors: U-Wai Lok; Pai-Chi Li;

Microbeamforming With Error Compensation

Abstract

One of the main issues in the development of 2-D arrays is the high system complexity due to the requirement for a large number of elements. The 2-D array systems suffer from high system complexity. The microbeamforming (MBF) method has been proposed to reduce the system complexity; however, distortions of MBF approach such as focusing errors of postbeamforming process result in broadening the main lobe and increasing the sidelobe and grating-lobe levels, which together degrade the image quality. As the presteered radio frequency (RF) data can be estimated from MBF data at the digital back end, better postbeamforming can be performed and higher image quality can be achieved. In this paper, a compensation approach is proposed to estimate the presteered RF data from MBF data by utilizing additional headers and compensation factors. The compensation factors and headers are estimated at the probe front end and then applied to the back-end digital system to reconstruct the required presteered RF data. As the absolute values of the MBF errors are modeled as a single-sided Gaussian distribution, the theoretical mean square error with the proposed method is approximately 2.75 times lower than that of its counterpart without compensation; this implies better reconstruction of presteered RF data can be achieved with the proposed method. The simulation results showed that the main lobe is improved, and the sidelobe and grating-lobe levels in both the lateral and elevation directions were improved by 11.73 and 19.12 dB, respectively, while the peak signal-to-noise ratios improved by 6-9 dB with the proposed method. The contrast-to-noise ratios also are enhanced by 0.5 dB when using the proposed method. Analog circuits are presented to demonstrate that this novel compensation method can be realized in practice. The reduction of cables and analog-to-digital converters are about seven-fold compared to fully sampled 2-D array systems as 4 by 4 channels are grouped for the proposed method as well.

Related Organizations
Keywords

Imaging, Three-Dimensional, Phantoms, Imaging, Image Processing, Computer-Assisted, Signal Processing, Computer-Assisted, Ultrasonography

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Average
Top 10%
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