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Record W2067149590 · doi:10.1117/1.3292007

Doppler optical coherence tomography for interventional cardiovascular guidance: in vivo feasibility and forward-viewing probe flow phantom demonstration

2010· article· en· W2067149590 on OpenAlexafffund
Nigel R. Munce, Graham A. Wright, Adrian Mariampillai, Beau A. Standish, Michael K. K. Leung, Louis Tan, Kenneth Lee, Brian K. Courtney, Aaron A. Teitelbaum, Bradley H. Strauss, I. Alex Vitkin, Victor X. D. Yang

Bibliographic record

VenueJournal of Biomedical Optics · 2010
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsToronto Metropolitan UniversitySunnybrook Health Science CentreUniversity of TorontoOntario Institute for Cancer Research
FundersCanadian Institutes of Health ResearchOntario Centres of Excellence
KeywordsOptical coherence tomographyImaging phantomDoppler effectPreclinical imagingBiomedical engineeringOpticsMedical imagingTomographyMedical physicsMedicineRadiologyPhysicsIn vivo

Abstract

fetched live from OpenAlex

We demonstrate the potential of a forward-looking Doppler optical coherence tomography (OCT) probe for color flow imaging in several commonly seen narrowed artery morphologies. As a proof of concept, we present imaging results of a surgically exposed thrombotic occlusion model that was imaged superficially to demonstrate that Doppler OCT can identify flow within the recanalization channels of a blocked artery. We present Doppler OCT images in which the flow is nearly antiparallel to the imaging direction. These images are acquired using a flexible 2.2-mm-diam catheter that used electrostatic actuation to scan up to 30 deg ahead of the distal end. Doppler OCT images of physiologically relevant flow phantoms consisting of small channels and tapered entrance geometries are demonstrated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2010
Admission routes2
Has abstractyes

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