Plane-wave imaging of ocular blood-flow
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".