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Acoustic Assessment of a Konjac–Carrageenan Tissue-Mimicking Material at 5–60 MHz

2014· article· en· W2102630002 on OpenAlexaboutno aff
David A. Kenwright, Neelaksh Sadhoo, Srinath Rajagopal, T. Anderson, Carmel M. Moran, Patrick W. F. Hadoke, Gillian A. Gray, Bajram Zeqiri, Peter R. Hoskins

Bibliographic record

VenueUltrasound in Medicine & Biology · 2014
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsnot available
FundersTechnological University DublinBritish Heart Foundation
KeywordsMaterials scienceAttenuationBroadbandUltrasoundAcousticsBiomedical engineeringAcoustic attenuationOpticsMedicinePhysics

Abstract

fetched live from OpenAlex

The acoustic properties of a robust tissue-mimicking material based on konjac–carrageenan at ultrasound frequencies in the range 5–60 MHz are described. Acoustic properties were characterized using two methods: a broadband reflection substitution technique using a commercially available preclinical ultrasound scanner (Vevo 770, FUJIFILM VisualSonics, Toronto, ON, Canada), and a dedicated high-frequency ultrasound facility developed at the National Physical Laboratory (NPL, Teddington, UK), which employed a broadband through-transmission substitution technique. The mean speed of sound across the measured frequencies was found to be 1551.7 ± 12.7 and 1547.7 ± 3.3 m s21, respectively. The attenuation exhibited a non-linear dependence on frequency, f (MHz), in the form of a polynomial function: 0.009787f2 1 0.2671f and 0.01024f2 1 0.3639f, respectively. The characterization of this tissue-mimicking material will provide reference data for designing phantoms for preclinical systems, which may, in certain applications such as flow phantoms, require a physically more robust tissuemimicking material than is currently available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.314
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2014
Admission routes1
Has abstractyes

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