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Record W2104635800 · doi:10.1109/ultsym.2001.992058

Non-linear scattering from microbubble contrast agents in the 14-40 MHz range

2002· article· en· W2104635800 on OpenAlexaff
David E. Goertz, Siu Wai Wong, Chien Ting Chin, Emmanuel Chérin, Peter N. Burns, F. Stuart Foster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsScatteringMicrobubblesPhysicsMaterials scienceOpticsUltrasoundAcoustics

Abstract

fetched live from OpenAlex

The non-linear scattering properties of microbubble contrast agents in the 14-40 MHz frequency range are investigated. Experiments were conducted as a function of pressure for two agents (Definity/sup TM/ and Optison/sup TM/) and polystyrene microbeads as control scatterers. For Definity/sup TM/, subharmonic (0.5f/sub 0/) and ultraharmonic (1.5f/sub 0/) scattering was substantial and strongly dependent on pressure up to 26 MHz. Optison/sup TM/ also exhibited sub- and ultraharmonic scattering, but these effects were weaker and found to persist up to 22 MHz. Second harmonic (2f/sub 0/) scattering for Definity/sup TM/, Optison/sup TM/ and the microbeads was strong and increased with pressure suggesting a significant component of 2f/sub 0/ agent signal was due to non-linear propagation. However, the ratio of 2f/sub 0/ to f/sub 0/ scattering was higher for agent than for microbeads (20 dB for Definity/sup TM/) after correcting for frequency dependent microbead scattering. A test for bubble destruction at 20 MHz revealed that a significant subpopulation of bubbles could be disrupted. We conclude that non-linear scattering can be produced with high frequency ultrasound technology and currently available agents. This suggests the possibility of implementing non-linear detection and destruction techniques at high frequencies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score1.000

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)0.0010.001

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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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 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

Citations16
Published2002
Admission routes1
Has abstractyes

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