Real-time optical-resolution photoacoustic microscopy using fiber-laser technology
Bibliographic record
Abstract
Optical-resolution photoacoustic microscopy (OR-PAM) is an emerging technology providing visualization of superficial structures in vivo with optical-absorption contrast. High resolution is possible as the lateral spatial resolution is determined by the optical spot size rather than acoustic detection. The imaging speed is dictated by both the beam scanning speed and the laser pulse repetition rate. We are developing a realtime OR-PAM system that uses a high repetition rate pulsed laser and high speed XY mirror galvanometers. We have demonstrated OR-PAM imaging by employing a diode-pumped pulsed Ytterbium fiber laser with a pulse repetition rate ranging from 20 kHz - 600 kHz, second harmonic generation at a wavelength of 532 nm and average output power up to 13 W. In our study, we utilized 0.13μJ ~1-ns pulses. A photoacoustic probe consisting of a 45-degree glass prism in an optical index-matching fluid is used to transmit the focused output of the laser to the sample and also to reflect exiting photoacoustic signals to an ultrasound transducer. Phantom studies with a ~7.5-μm carbon fiber demonstrate the ability to image with ~7-μm optical lateral spatial resolution. Combined with a fast-scanning mirror oscillating at 800 (B-scan) lines per second, we demonstrate a system capable of C-scan imaging at 4 frames per second. These near-realtime frame-rates should permit clinical applications.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".