ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2019
Effect of a Thin Fluid Layer on Surface Wave Speed Measurements: A Lung Phantom Study.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- A Pilot Study of Wet Lung Using Lung Ultrasound Surface Wave Elastography in an Ex Vivo Swine Lung Model.Applied sciences (Basel, Switzerland) · 2019Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Lung ultrasound (US) surface wave elastography (SWE) is a novel technique that measures superficial lung tissue elastic properties. A thin pleural fluid layer covers a lung, but its effect on lung measurements in SWE is unknown. We modeled a lung and pleural fluid with sponges and a thin layer of US transmission gel. Sponge surface wave speeds measured from SWE were compared for sponges without and with the thin US gel layer at 3 wave excitation frequencies. The comparison showed that the sponge surface wave speed measurements were not affected by the thin gel layer.
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Registered trials
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.