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Record W1990961782 · doi:10.1117/12.808041

Flow of blood-saline mixtures studied by time-domain optical coherence tomography

2009· article· en· W1990961782 on OpenAlexaff
Dan P. Popescu, Michael G. Sowa

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOptical coherence tomographyLaminar flowBlood flowOpticsCoherence (philosophical gambling strategy)Materials scienceTomographySalineBiomedical engineeringOptical tomographyTurbulencePhysicsMechanicsMedicineRadiology

Abstract

fetched live from OpenAlex

In-vitro analysis of flowing blood-saline mixtures is performed by time-domain optical coherence tomography imaging. The mixtures contain blood in concentrations ranging from 100% to 20%. For each image, a corresponding compounded profile is obtained by adding one thousand adjacent A-scans. The compounded profiles are used for characterizing the optical coherence tomography signal as it propagates within the studied blood-saline mixtures. The results obtained point toward the possibility of acquiring intra-vascular images of arterial tissue that is located behind slabs of flowing blood-saline. A threshold in the propagation distance beyond which the recorded signal becomes dominated by its multiple scattered components is established along the compounded profiles. The threshold location, at a depth of ~0.6 mm, is independent of blood concentration. Further investigation of the compounded profiles reveals that the region extending to a maximum depth of about 200 μm from the point where the probing beam enters blood-saline mixtures could reveal information about the flow regime. This opens the possibility of another application for time-domain optical coherence tomography in intravascular imaging: assessing the flow regime, i.e. laminar or turbulent.

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 categoriesMeta-epidemiology (narrow)
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 score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.216
Teacher spread0.209 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207