Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
Bibliographic record
Abstract
Intraluminal photoacoustic (PA) imaging has the potential for providing physiological and functional information in wide-ranging clinical applications. Along with endoluminal ultrasound transducers, these applications require compact light delivery devices which can deliver high-energy ns-pulsed laser to the target region. In this work, we describe the design, method of fabrication and characterization of a new compact, side-fire optical fiber that can deliver high-energy laser pulses for PA imaging. Side-fire illuminators were fabricated using UV laser ablation to create windows on the side of a 1.5 mm diameter single core, multi-mode optical fiber with a reflective silver coating and a beveled end. Devices with 10 mm, 20 mm, and 30 mm window lengths were fabricated and their beam profiles characterized. Elongated side-fire fibers with −6 dB beam size up to 30.79 mm × 5.5 mm were developed. A side-fire to total output ratio of up to 0.69 and a side fire efficiency of up to 40%, relative to a standard front-fire fiber, were achieved. We evaluated the effects of high-energy ns-pulsed light propagation on the fiber by coupling the fiber to 18 mJ or 100 MW/cm2 (at 750 nm) beam from a Q-switched laser. The PA imaging with the fiber was demonstrated by detecting India ink targets embedded in chicken breast tissue over the full length of a 20 mm illumination window and over a 100° angle and by visualizing in vivo the rat ear vasculature.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".