Multispectral photoacoustic remote sensing microscopy using 532nm and 266nm excitation wavelengths
Bibliographic record
Abstract
Photoacoustic remote sensing (PARS) is a non-contact imaging modality that is based on the optical absorption contrast of endogenous molecules. PARS has shown promise in vascular imaging, blood oxygenation estimation, and virtual biopsy without the need for exogenous labels. Here we demonstrate simultaneous imaging of cell nuclei and blood using UV and visible excitation wavelengths. This is important for decoupling blood signals from cell nuclei signals in removed tissue and resection beds. A 532nm fiber laser is split with one light path frequency doubled using a CLBO crystal to 266nm. These two wavelength lasers are co-aligned and co-focused with a 1310nm interrogation beam and using a reflective objective to image microvasculature and cell nuclei with intrinsic optical absorptions at 532nm and 266nm, respectively. These images are taken serially and co-registered with lateral resolutions of 1.2μm and 0.44μm respectively. Co-alignment using multiple wavelengths is demonstrated using carbon fiber phantoms. We imaged both paraffin embedded tissue and in vivo mouse ear. Cell nuclei in sectioned tissues were clearly visualized with a SNR of 42dB while hemoglobin demonstrated an SNR of 39dB. In vivo cell nuclei and vasculature images produced an SNR up to 40dB and 35dB, respectively.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".