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Record W1991133593 · doi:10.1088/0031-9155/59/23/7541

Visualization of ultrasound induced cavitation bubbles using the synchrotron x-ray Analyzer Based Imaging technique

2014· article· en· W1991133593 on OpenAlexafffund
Zahra Izadifar, George Belev, Mohammad Izadifar, Zohreh Izadifar, Dean Chapman

Bibliographic record

VenuePhysics in Medicine and Biology · 2014
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersCanadian Institutes of Health ResearchFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaMinisterio de Ciencia e Innovación
KeywordsCavitationUltrasoundSynchrotronMaterials scienceSonochemistryUltrasonic sensorBiomedical engineeringAcousticsSpectrum analyzerOpticsPhysicsMedicine

Abstract

fetched live from OpenAlex

Observing cavitation bubbles deep within tissue is very difficult. The development of a method for probing cavitation, irrespective of its location in tissues, would improve the efficiency and application of ultrasound in the clinic. A synchrotron x-ray imaging technique, which is capable of detecting cavitation bubbles induced in water by a sonochemistry system, is reported here; this could possibly be extended to the study of therapeutic ultrasound in tissues. The two different x-ray imaging techniques of Analyzer Based Imaging (ABI) and phase contrast imaging (PCI) were examined in order to detect ultrasound induced cavitation bubbles. Cavitation was not observed by PCI, however it was detectable with ABI. Acoustic cavitation was imaged at six different acoustic power levels and six different locations through the acoustic beam in water at a fixed power level. The results indicate the potential utility of this technique for cavitation studies in tissues, but it is time consuming. This may be improved by optimizing the imaging method.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.193

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 head0.074
GPT teacher head0.351
Teacher spread0.277 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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