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Record W2109133888 · doi:10.1088/0031-9155/53/5/004

<i>In vitro</i>characterization of the subharmonic ultrasound signal from Definity microbubbles at high frequencies

2008· article· en· W2109133888 on OpenAlexaff
Kevin L.Y. Cheung, Olivier Couture, Peter D. Bevan, Emmanuel Chérin, R. P. Williams, Peter N. Burns, F. Stuart Foster

Bibliographic record

VenuePhysics in Medicine and Biology · 2008
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Cancer InstituteBristol-Myers Squibb
KeywordsSubharmonicMicrobubblesBubbleHarmonicUltrasoundSIGNAL (programming language)PhysicsAcousticsMaterials scienceContrast (vision)OpticsNonlinear systemMechanicsComputer science

Abstract

fetched live from OpenAlex

Ultrasound microbubble contrast agents have been demonstrated to scatter subharmonic energy at one-half the driving frequency. At ultrasound frequencies in the 20-40 MHz range, the subharmonic offers the potential to differentiate the blood in the microcirculation from the surrounding tissue. It is unknown whether current contrast agents, manufactured to be resonant between 2 and 12 MHz, are ideal for subharmonic imaging at higher frequencies. We performed numerical simulations of the Keller-Miksis model for the behavior of a single bubble and experimental investigations of Definity microbubbles in water. The results supported the hypothesis that off-resonant bubbles, excited at their second harmonic, may be primarily responsible for the observed subharmonic energy. For frequencies between 20 and 32 MHz and 32 and 40 MHz, the optimal bubble diameters for the generation of subharmonics in vitro were determined experimentally to be 1.2-5 microm and less than 1.2 microm, respectively. Definity may be a suitable ultrasound contrast agent for subharmonic imaging at 20 MHz with peak-negative pressures between 380 and 590 kPa and pulses greater than or equal to four cycles in duration.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.179
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.079
GPT teacher head0.260
Teacher spread0.181 · 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

Citations46
Published2008
Admission routes1
Has abstractyes

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