Numerical correction of bi-directional scanning distortion in FDML OCT
Bibliographic record
Abstract
Optical Coherence Tomography (OCT) is a non-invasive imaging technique, essential in medical diagnostics due to its ability to produce high-resolution images of internal structures of biological tissues. One of the unique features of the FDML based MHz-OCT is the optical buffering that increases the A-scan rate by creating successive time-delayed copies of the original sweep. However, due to the optical buffering, numerous studies have reported that A-lines originates from different buffer can have different amplitude and phase. Another challenge associated with high A-scan laser source is to pair with the high-speed mechanical scanning protocol to avoid oversampling. Most of the FDML based OCT system is oversampled due to the mechanical limitation of the galvanometer. In this paper, an optimization method is applied to the backward scanning data to eliminate the distortions. Moreover, the phase and amplitude misalignment issues are also numerically corrected. The amplitude inconsistencies in the acquired interferogram are also addressed and solved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".