Evidence map›Paper›PMID 29882859›Full record

ArticleSensors (Basel, Switzerland)2018

Holey-Cavity-Based Compressive Sensing for Ultrasound Imaging.

Ashkan Ghanbarzadeh-Dagheyan, Chang Liu, Ali Molaei, Juan Heredia, Jose Martinez Lorenzo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ashkan Ghanbarzadeh-DagheyanMechanical Engineering Department, Northeastern University, Boston, MA 02115, USA. ghanbarzadehdaghe.a@husky.neu.edu.ORCID 0000-0003-3640-2821
Chang LiuMechanical Engineering Department, Northeastern University, Boston, MA 02115, USA. liu.chang4@husky.neu.edu.
Ali MolaeiElectrical Engineering Department, Northeastern University, Boston, MA 02115, USA. molaei.a@husky.neu.edu.
Juan HerediaMechanical Engineering Department, Northeastern University, Boston, MA 02115, USA. j.herediajuesas@neu.edu.
Jose Martinez LorenzoMechanical Engineering Department, Northeastern University, Boston, MA 02115, USA. j.martinez-lorenzo@neu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of solid cavities around electromagnetic sources has been recently reported as a mechanism to provide enhanced images at microwave frequencies. These cavities are used as measurement randomizers; and they compress the wave fields at the physical layer. As a result of this compression, the amount of information collected by the sensing array through the different excited modes inside the resonant cavity is increased when compared to that obtained by no-cavity approaches. In this work, a two-dimensional cavity, having multiple openings, is used to perform such a compression for ultrasound imaging. Moreover, compressive sensing techniques are used for sparse signal retrieval with a limited number of operating transceivers. As a proof-of-concept of this theoretical investigation, two point-like targets located in a uniform background medium are imaged in the presence and the absence of the cavity. In addition, an analysis of the sensing capacity and the shape of the point spread function is also carried out for the aforementioned cases. The cavity is designed to have the maximum sensing capacity given different materials and opening sizes. It is demonstrated that the use of a cavity, whether it is made of plastic or metal, can significantly enhance the sensing capacity and the point spread function of a focused beam. The imaging performance is also improved in terms cross-range resolution when compared to the no-cavity case.

Indexed as

compressive sensingultrasound cavityultrasound imaging

Identifiers

PMID29882859
PMCPMC6021799

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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.