Wavefront sensorless adaptive optics optical coherence tomography for multiphoton retinal imaging (Conference Presentation)
Bibliographic record
Abstract
Two-photon excited fluorescence (TPEF) for in-vivo retinal imaging is an emerging tool for vision science. TPEF has multiple benefits in comparison to conventional confocal fluorescence scanning laser ophthalmoscopy for retinal imaging, including better axial resolution and the ability to use infrared excitation light for imaging the highly photosensitive tissue in the retina. TPEF is very sensitive to the focused spot size, which is enlarged by aberrations induced by the refractive elements of the mouse eye when imaging with a large numerical aperture. Our system begins with a femtosecond pulsed laser for two-photon excitation, which is also sufficiently spectrally broadband to allow for an optical coherence tomography (OCT) sub-system to guide aberration correction. The OCT system operated at 1 volumes/second with our custom GPU accelerated real-time processing. Our lens-based optical design features two deformable elements, one with large stroke for focus control on the retina and the other with multiple actuators for aberration correction. Our wavefront-sensorless adaptive optics (SAO) is driven by a modal search with a sharpness quality metric on the en-face OCT image of the selected retinal layer. After optimization, the speed was increased to 10 fps for TPEF imaging to allow for streaming and averaging ~200 frames per image. To demonstrate the system capabilities, we performed in-vivo retinal fluorescein angiography using TPEF. Our results demonstrate depth-resolved aberration correction with the SAO-OCT to increase the TPEF signal intensity. We also present TPEF at multiple vascular layers in the mouse retina alongside the volumetric OCT to localize the vessels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".