Towards non-contact photoacoustic imaging [review]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Photoacoustic imaging (PAI) takes advantage of both optical and ultrasound imaging properties to visualize optical absorption with high resolution and contrast. Photoacoustic microscopy (PAM) is usually categorized with all-optical microscopy techniques such as optical coherence tomography or confocal microscopes. Despite offering high sensitivity, novel imaging contrast, and high resolution, PAM is not generally an all-optical imaging method unlike the other microscopy techniques. One of the significant limitations of photoacoustic microscopes arises from their need to be in physical contact with the sample through a coupling media. This physical contact, coupling, or immersion of the sample is undesirable or impractical for many clinical and pre-clinical applications. This also limits the flexibility of photoacoustic techniques to be integrated with other all-optical imaging microscopes for providing complementary imaging contrast. To overcome these limitations, several non-contact photoacoustic signal detection approaches have been proposed. This paper presents a brief overview of current non-contact photoacoustic detection techniques with an emphasis on all-optical detection methods and their associated physical mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it