Comparative Study of Optical Coherence Tomography Angiography and Phase-Resolved Doppler Optical Coherence Tomography for Measurement of Retinal Blood Vessels Caliber
Bibliographic record
Abstract
Purpose: To compare the accuracy of Doppler optical coherence tomography (DOCT) and OCT angiography (OCTA) for measuring retinal blood vessel caliber at different flow rates. Methods: A research-grade 1060-nm OCT system with 3.5-μm axial resolution in retinal tissue and 92,000 A scan/s image acquisition rate was used in this study. DOCT and OCTA measurements were acquired both from a flow phantom and in vivo from retinal blood vessels in six male Brown Norway rats. The total retinal blood flow (TRBF) was modified from baseline to 70% and 20% of baseline by reducing the ocular perfusion pressure (OPP). The retinal blood vessel caliber (RBVC) was measured from OCTA and DOCT images. The caliber measurements were conducted by two separate graders using a custom MATLAB-based image processing algorithm. Results: The RBVC measured with OCTA and DOCT for normal blood flow rates were not significantly different (56.69 ± 12.17 and 57.17 ± 9.46 μm, P = 0.27, respectively). However, significant differences were detected when TRBF was reduced to 70% (55.69 ± 11.56 vs. 50.62 ± 8.85 μm, P < 0.01) and 20% (50.29 ± 9.29 vs. 44.88 ± 7.13 μm, P < 0.01) of baseline. Conclusions: Reduced TRBF resulted in inaccuracy of the RBVC measurements with DOCT in both the phantom and animal study. This result suggests that OCTA is a more accurate tool for RBVC evaluation when applied to retinal diseases associated with reduced TRBF, such as glaucoma and diabetic retinopathy. Translational Relevance: Results from this study are directly applicable to clinical studies of retinal blood flow measured with OCTA and DOCT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".