Sensorless adaptive-optics optical coherence tomographic angiography
Bibliographic record
Abstract
Optical coherence tomographic angiography (OCTA) can image the retinal blood flow but visualization of the capillary caliber is limited by the low lateral resolution. Adaptive optics (AO) can be used to compensate ocular aberrations when using high numerical aperture (NA), and thus improve image resolution. However, previously reported AO-OCTA instruments were large and complex, and have a small sub-millimeter field of view (FOV) that hinders the extraction of biomarkers with clinical relevance. In this manuscript, we developed a sensorless AO-OCTA prototype with an intermediate numerical aperture to produce depth-resolved angiograms with high resolution and signal-to-noise ratio over a 2 × 2 mm FOV, with a focal spot diameter of 6 µm, which is about 3 times finer than typical commercial OCT systems. We believe these parameters may represent a better tradeoff between resolution and FOV compared to large-NA AO systems, since the spot size matches better that of capillaries. The prototype corrects defocus, astigmatism, and coma using a figure of merit based on the mean reflectance projection of a slab defined with real-time segmentation of retinal layers. AO correction with the ability to optimize focusing in arbitrary retinal depths - particularly the plexuses in the inner retina - could be achieved in 1.35 seconds. The AO-OCTA images showed greater flow signal, signal-to-noise ratio, and finer capillary caliber compared to commercial OCTA. Projection artifacts were also reduced in the intermediate and deep capillary plexuses. The instrument reported here improves OCTA image quality without excessive sacrifice in FOV and device complexity, and thus may have potential for clinical translation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".