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Record W2104165424 · doi:10.1109/ultsym.1990.171624

Determination of 2-D velocity vectors using color Doppler ultrasound

2002· article· en· W2104165424 on OpenAlex
T. Tamura, R.S.C. Cobbold, K.W. Johnston

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Symposium on Ultrasonics · 2002
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVector fieldDoppler effectFlow (mathematics)Flow velocityVector flowPhysicsComputationColor dopplerVelocity vectorArtificial intelligenceComputer visionOpticsGeometryComputer scienceMathematicsImage (mathematics)MechanicsAlgorithmImage segmentationUltrasonographyMedicine

Abstract

fetched live from OpenAlex

Color Doppler flow images were obtained from two beam directions, and the velocity vectors in the flow field were computed for each pixel point. The technique was applied to an end-to-side anastomosis model. As expected, the directions of velocity vectors change gradually as flow travels through the anastomosis. Highly skewed velocity profiles along with a stagnant flow region were visualized across the lumen of the host artery. High radial velocity components associated with the secondary flow were clearly revealed about 1-2 diameters downstream from the junction. Based on a single Doppler image, this region with radial flow could be incorrectly integrated as a region with high axial velocities. The vector computation clearly resolved this problem. True velocity magnitudes were then used to create color flow images independent of the Doppler angle.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

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 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 score0.141
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
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.264
Teacher spread0.241 · 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