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Record W2325112228 · doi:10.1117/12.2218160

Characterization of various tissue mimicking materials for medical ultrasound imaging

2016· article· en· W2325112228 on OpenAlexaff
Audrey Thouvenot, Tamie L. Poepping, Terry M. Peters, Elvis C. S. Chen

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsAttenuationMaterials scienceAttenuation coefficientUltrasoundPolydimethylsiloxaneSpeed of soundVolume (thermodynamics)Analytical Chemistry (journal)OpticsAcousticsComposite materialChemistryPhysics

Abstract

fetched live from OpenAlex

Tissue mimicking materials are physical constructs exhibiting certain desired properties, which are used in machine calibration, medical imaging research, surgical planning, training, and simulation. For medical ultrasound, those specific properties include acoustic propagation speed and attenuation coefficient over the diagnostic frequency range. We investigated the acoustic characteristics of polyvinyl chloride (PVC) plastisol, polydimethylsiloxane (PDMS), and isopropanol using a time-of-light technique, where a pulse was passed through a sample of known thickness contained in a water bath. The propagation speed in PVC is approximately 1400ms<sup>-1</sup> depending on the exact chemical composition, with the attenuation coefficient ranging from 0:35 dB cm<sup>-1</sup> at 1MHz to 10:57 dB cm<sup>-1</sup> at 9 MHz. The propagation speed in PDMS is in the range of 1100ms<sup>-1</sup>, with an attenuation coefficient of 1:28 dB cm<sup>-1</sup> at 1MHz to 21:22 dB cm<sup>-1</sup> at 9 MHz. At room temperature (22 &deg;C), a mixture of water-isopropanol (7:25% isopropanol by volume) exhibits a propagation speed of 1540ms<sup>-1</sup>, making it an excellent and inexpensive tissue-mimicking liquid for medical ultrasound imaging.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2016
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicUltrasound Imaging and ElastographyFrench-language works237,207