Evidence map›Paper›PMID 28432447›Full record

ArticleJournal of medical ultrasonics (2001)2018

Twofold minimum variance beamforming for enhanced ultrasound imaging.

Sayed Mahmoud Sakhaei, Seyede Elham Shamsian

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Article in Journal of medical ultrasonics (2001), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sayed Mahmoud SakhaeiDepartment of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran. smsakhaei@nit.ac.ir.ORCID http://orcid.org/0000-0003-3961-4395
Seyede Elham ShamsianDepartment of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo calculate the optimal weights of a minimum variance (MV) beamformer, the covariance matrix of the received data should be estimated. In ultrasound imaging, it is done through spatial smoothing. However, this technique reduces the effective aperture and consequently limits the attainable resolution. To mitigate this effect, we propose a new configuration for applying an adaptive beamforming method.

methodsThe method is based on applying MV in a twofold configuration such that the outputs of the subarrays are combined through an adaptive MV-like beamformer, instead of simply summing them. In this way, a fully adaptive beamformer is obtained.

resultsApplying the method on a phantom containing point targets has shown that off-axis signals are attenuated more in comparison to MV. This results in a lowered mainlobe width and decreased sidelobe levels. Moreover, simulation results of a cyst phantom confirm the superiority of the method over the MV in terms of contrast.

conclusionThe method yields significant improvement in the resolving capability and the contrast compared with the conventional MV. At the presence of the steering vector errors, these superiorities were achieved at a cost of slightly more errors in estimating the reflectivity coefficients.

Indexed as

AlgorithmsComputer SimulationCystsImage Processing, Computer-AssistedModels, BiologicalPhantoms, ImagingSignal Processing, Computer-AssistedUltrasonographyAdaptive beamformingMinimum varianceSpatial smoothingUltrasound imaging

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.