Speckle-resolved optical coherence tomography for mesoscopic imaging within scattering media
Bibliographic record
Abstract
Light scattering poses a challenge for imaging deep in scattering media as the ballistic light exponentially attenuates with depth. In contrast to the ballistic light, the multiply scattered light penetrates deeper and also contains information about the sample. One technique to image deeper is to selectively detect only a subset of the multiply scattered light, namely the 'snake' photons, which are predominantly forward scattered and retain more direct information than the more strongly scattered light. In this work, we develop a technique, termed speckle-resolved optical coherence tomography (srOCT), for efficiently detecting these 'snake' photons to enable imaging deeper in scattering media. The system couples spatio-angular filtering with speckle-resolved interferometric detection to preferentially and efficiently detect the weakly scattered 'snake' photons. With our proof-of-concept system, we demonstrate depth-resolved imaging beyond the ballistic limit, up to a depth of 90 round-trip MFPs in a scattering phantom and a depth of 4.5 mm of chicken tissue at 0.4 mm axial and lateral resolution.
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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) | 0.001 | 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 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".