Absolute depth-resolved optic axis measurement with catheter-based polarization sensitive optical coherence tomography
Bibliographic record
Abstract
Imaging depth-resolved birefringence and optic axis orientation with polarization sensitive optical coherence tomography (PS-OCT) unveils details of tissue structure and organization that can be of high pathophysiologic, mechanistic, and diagnostic value. For catheter-based PS-OCT, the dynamic rotation of the fiber optic probe, in addition to the polarization effects of the system components, complicates the reliable and robust reconstruction of the sample's optic axis orientation. Addressing this issue, we present a new method for the reconstruction of absolute depth-resolved optic axis orientation in catheter-based PS-OCT by using the intrinsic retardance of the protecting catheter sheath as a stable guide star signal. Throughout the paper, we rigorously inspect the retardance and optic axis orientation of the sheath and validate our method by imaging a birefringent phantom with known optic axis orientation. Reconstructing the optic axis orientation of the phantom, placed at different locations around the catheter, we measured an average absolute deviation (AAD) for the mean optic axis orientation over cross-sectional images of 3.28°, even with significant bending stress on the catheter. This corresponds to an almost three-fold improvement compared to our earlier method (optic axis AAD of 9.41°). We finally highlight the capability of our reconstruction with stereotactic catheter-based PS-OCT of a fresh sheep brain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".