Coherence-gated photoacoustic remote sensing microscopy (Conference Presentation)
Bibliographic record
Abstract
Photoacoustic remote sensing (PARS) microscopy is a novel photoacoustic modality which provides non-contact reflection-mode operation within optical penetration regimes. It has thus far demonstrated exceptional in vivo imaging capabilities with high signal-to-noise (greater than 70dB) and sub-cellular lateral resolution (on the order of 600 nm). Moreover, being non-contact opens a wide range of previously inaccessible imaging targets where acoustic coupling to the sample is impractical. One disadvantage of the technique however is the lack of time-gated depth discrimination which has long been a staple of more conventional photoacoustic methods. Rather, depth-resolving ability has been solely defined by the optical section provided by the primary objective lens. Here a pulsed short-wave infrared low-coherence detection beam in a spectral-domain OCT system is used to probe depth-resolved reflectivity before and immediately after visible pulsed excitation. A difference image between these A-scans reveals signals with optical absorption contrast. Simulations based on recently-developed time-domain modeling of low-coherence PARS reflectivity changes is used to generate software-phantom images. We used a 1310-nm ns-pulsed interrogation source with 45nm linewidth, along with a 532-nm ns-pulsed excitation beam. The effects of various material and apparatus parameters are discussed along with extensive analytical and simulation results. These showcase the potential capabilities of the approach, such as depth resolved spectral unmixing (with oxygen saturation) and discrimination of blood vessels in highly scattering media, along with foreseeable limitations and potential implementation issues.
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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) | 0.066 | 0.010 |
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".