MétaCan
Menu
Back to cohort
Record W2985977782 · doi:10.1121/1.5137064

Plane-wave imaging of ocular blood-flow

2019· article· en· W2985977782 on OpenAlexaff
Ronald H. Silverman, Raksha Urs, Jeffrey A. Ketterling, Billy Y. S. Yiu, Alfred C. H. Yu

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2019
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBlood flowGlaucomaOptical coherence tomographyChoroidRetinaMedicineBiomedical engineeringComputer scienceOpticsOphthalmologyRadiologyPhysics

Abstract

fetched live from OpenAlex

Blood-flow plays an important role in pathogenesis and progression of many ocular diseases. While optical coherence tomography angiography (OCT-A) has revolutionized depiction of the retinal vasculature, it provides little information regarding flow velocities and cannot visualize the arteries and veins supplying and draining the eye. We implemented plane-wave ultrasound methods to address these shortcomings using the Verasonics Vantage-128 with L22-14 linear array probes. We are performing clinical studies of glaucoma, retinopathy of prematurity, preeclampsia, vascular malformations and tumors. Scans are typically acquired for 3 s, capturing 2-3 cardiac cycles. Compounded data (2–6 angles) acquired at a 1–6 kHz PRF are post-processed using a singular value decomposition filter to suppress stationary structures and power-Doppler images are generated. By selecting areas-of-interest representing specific vessels or the choroid (the vascular tissue underlying and supplying the retina), spectrograms are produced, enabling measurement of flow velocities and resistive indices for each vascular component. We are also performing pre-clinical studies of blood-flow in the rat eye, with and without introduction of contrast microbubbles, and are developing super-resolution methods that approach OCT in resolution for improved depiction of the microvasculature as we develop the rat as a model of glaucoma.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.264

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 science0.0010.000
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 head0.006
GPT teacher head0.203
Teacher spread0.196 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicOptical Coherence Tomography ApplicationsFrench-language works237,207