Effective interscan time for enhanced <i>in vivo</i> choriocapillaris imaging with OCT angiography
Bibliographic record
Abstract
imaging of the choriocapillaris (CC) remains challenging due to its dense microvascular structure and low reflectivity. While previous studies have explored various scanning protocols to enhance CC visualization, most approaches rely on oversampling, beam size adjustments, large numbers of repetitive B-scans (BM), and volume registration. However, interscan time-a critical parameter that influences flow contrast and vascular detail-has been largely overlooked. In this Letter, we introduce interscan time as a novel, to the best of our knowledge, CC imaging parameter and propose an optimized OCTA protocol by leveraging a 1.6 MHz FDML swept-source laser and step-bidirectional scanning method to investigate its impact across beam sizes and BM-scans. Our findings reveal that shorter interscan time significantly improves CC visualization by enhancing vessel contrast and preserving microvascular details, enabling better clinical assessment of retinal diseases such as age-related macular degeneration and diabetic retinopathy.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".