Rapid High-Resolution Mosaic Acquisition for Photoacoustic Remote Sensing
Bibliographic record
Abstract
Mechanical stages are routinely used to scan large expanses of biological specimens in photoacoustic imaging. This is primarily due to the limited field of view (FOV) provided by optical scanning. However, stage scanning becomes impractical at higher scanning speeds, or potentially unfeasible with heavier samples. Also, the slow scan-rate of the stages makes high resolution scanning a time-consuming process. Some clinical applications such as microsurgery require submicron resolution in a reflection-mode configuration necessitating a method that can acquire large field of views with a small raster scanning step size. In this study, we describe a method that combines mechanical stages with optical scanning for the rapid acquisition of high-resolution large FOVs. Optical scanning is used to acquire small frames in a two-dimensional grid formed by the mechanical stages. These frames are captured with specific overlap for effective image registration. Using a step size of 200 nm, we demonstrate mosaics of carbon fiber networks with FOVs of 0.8 × 0.8 mm2 captured in under 70 s with 1.2 µm image resolution. Larger mosaics yielding an imaging area of 3 × 3 mm2 are also shown. The method is validated by imaging a 1 × 1 mm2 section of unstained histopathological human tissue.
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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) | 0.003 | 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".