Toward co-localized OCT surveillance of laser therapy using real-time speckle decorrelation (Conference Presentation)
Bibliographic record
Abstract
Laser therapy has been used to perform both ablation and coagulation of diseased tissue. To avoid over or under exposure, monitoring such therapies with a cost-effective method remains an issue however. We present an integrated solution based on an optical coherence tomography (OCT) system allowing simultaneous imaging, quantitative monitoring and therapy delivery in real-time. The system exploits a double-clad fiber coupler (DCFC) to inject the OCT signal into the double-clad fiber (DCF) core and the therapy laser into the inner cladding making them co-localized. The single fiber solution permits both imaging and therapy at the same time. Furthermore, the DCFC allows the implementation of our technique in any OCT system sharing the same wavelength bandwidth. Therapy monitoring is achieved by measuring the speckle intensity decorrelation. During coagulation, the optical properties of the tissue start to vary, thereby changing the speckle intensity pattern seen in the OCT tomograms. The proposed algorithm includes both novel motion and noise corrections, extending the usable monitoring depth. Furthermore, the code has been optimized to run during therapy providing real-time monitoring. In a proof of concept experiment, a system was built with a 532 nm CW laser for therapy and a 1310 nm swept-source laser for OCT imaging. We present ex-vivo cross-sectional imaging and monitoring during therapy. Experimental results were validated against Monte-Carlo simulations and visual inspection.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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 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".