Advances in engineering of high contrast CARS imaging endoscopes
Bibliographic record
Abstract
The translation of CARS imaging towards real time, high resolution, chemically selective endoscopic tissue imaging applications is limited by a lack of sensitivity in CARS scanning probes sufficiently small for incorporation into endoscopes. We have developed here a custom double clad fiber (DCF)-based CARS probe which is designed to suppress the contaminant Four-Wave-Mixing (FWM) background generated within the fiber and integrated it into a fiber based scanning probe head of a few millimeters in diameter. The DCF includes a large mode area (LMA) core as a first means of reducing FWM generation by ~3 dB compared to commercially available, step-index single mode fibers. A micro-fabricated miniature optical filter (MOF) was grown on the distal end of the DCF to block the remaining FWM background from reaching the sample. The resulting probe was used to demonstrate high contrast images of polystyrene beads in the forward-CARS configuration with > 10 dB suppression of the FWM background. In epi-CARS geometry, images exhibited lower contrast due to the leakage of MOF-reflected FWM from the fiber core. Improvements concepts for the fiber probe are proposed for high contrast epi-CARS imaging to enable endoscopic implementation in clinical tissue assessment contexts, particularly in the early detection of endoluminal cancers and in tumor margin assessment.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".