Mosaicing for fast wide-field-of-view optical-resolution photoacoustic microscopy
Bibliographic record
Abstract
The acquisition speed of previously reported mechanically-scanned Optical-Resolution Photoacoustic Microscopy (OR-PAM) systems has been limited by both laser pulse repetition rate and mechanical scanning speed. In this paper we introduce a mosaicing scheme wherein a grid of small sub-mm-scale field-of-view (FOV) patches are acquired in 0.5s per patch, and a 3-axis stepper-motor system is used to mechanically move the object to be imaged from patch-to-patch in less than 0.5s. Patch images are aligned and stitched to generate a large FOV image composite. This system retains the SNR-advantages of focused-transducer OR-PAM systems, and is a hybrid approach between optical-scanning and mechanical scanning. With this strategy we reduce the data acquisition time of previously reported large-FOV systems by a factor of around 23. SCID hairless mice are imaged. The wide-FOV, high-speed data acquisition OR-PAM system broadens the potential applications of the imaging modality.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".