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Record W1973208631 · doi:10.1121/1.3277200

Ultrasound characterization of red blood cell aggregation with intervening attenuating tissue-mimicking phantoms

2010· article· en· W1973208631 on OpenAlexafffund
Émilie Franceschini, François T.H. Yu, François Destrempes, Guy Cloutier

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversité de Montréal
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of HealthHeart and Stroke Foundation of Canada
KeywordsAttenuationBiomedical engineeringMaterials scienceBlood flowTransducerAttenuation coefficientUltrasoundIn vivoUltrasonic sensorScatteringOpticsAcousticsMedicinePhysicsRadiology

Abstract

fetched live from OpenAlex

The analysis of the ultrasonic frequency-dependent backscatter coefficient of aggregating red blood cells reveals information about blood structural properties. The difficulty in applying this technique in vivo is due to the frequency-dependent attenuation caused by intervening tissue layers that distorts the spectral content of signals backscattered by blood. An optimization method is proposed to simultaneously estimate tissue attenuation and blood structure properties, and was termed the structure factor size and attenuation estimator (SFSAE). An ultrasound scanner equipped with a wide-band 25 MHz probe was used to insonify porcine blood sheared in both Couette and tubular flow devices. Since skin is one of the most attenuating tissue layers during in vivo scanning, four skin-mimicking phantoms with different attenuation coefficients were introduced between the transducer and the blood flow. The SFSAE gave estimates with relative errors below 25% for attenuations between 0.115 and 0.411 dBMHz and kR<2.08 (k being the wave number and R the aggregate radius). The SFSAE can be useful to examine in vivo and in situ abnormal blood conditions suspected to promote pathophysiological cardiovascular consequences.

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)0.000
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 score0.093
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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

Citations75
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUltrasound Imaging and ElastographyFrench-language works237,207