Limiting factors to the OCT axial resolution for in-vivo imaging of human and rodent retina in the 1060nm wavelength range
Bibliographic record
Abstract
A computational model was developed to evaluate the limitations to the highest axial resolution, achievable with ultrahigh resolution optical coherence tomography (UHROCT) in the 1060 nm wavelength region for in-vivo imaging of the human and rodent retina. The model considers parameters such as the wavelength dependent water absorption, the average length of the human and rodent eyes, and the power limitations for the imaging beam as defined in the ANSI standard. A custom-built light source with re-shaped spectrum was used to verify experimentally the results from the computational model. Axial OCT resolution of 4.2 microm and 7.7 microm was measured from a mirror reflection with the custom light source by propagating the imaging beam through water cells with 5 mm and 25 mm thickness, corresponding to the average axial length of the rodent and human eye respectively. Assuming an average refractive index of 1.38 for retinal tissue, the expected axial OCT resolution in the rodent and human retina is 3 microm and 5.7 microm respectively. Retinal tomograms acquired in-vivo from the rat eye with the modified light source show clear visualization of all intraretinal layers, as well as a network of capillaries (approximately 10 microm in diameter) in the inner- and outer plexiform layers of the retina.
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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 | 0.003 |
| 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 | 0.001 | 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".