A tutorial in photoacoustic microscopy and tomography signal processing methods
Bibliographic record
Abstract
Over the last two and a half decades, photoacoustic (PA) imaging has become an important area of research in biomedical optics. Combining the high contrast of optical imaging with the high spatial resolution of ultrasound (US) imaging, PA imaging can simultaneously visualize anatomical structures while interrogating their functionality through multiwavelength optical spectroscopy. Alongside technological developments and imaging applications in optical and acoustic resolution PA imaging, a family of PA signal analysis techniques can extract additional information about the sample being imaged. This Tutorial focuses on techniques that rely on the analysis of PA signals in a manner similar to that in the complimentary field of quantitative ultrasound (QUS) imaging of soft tissues. In QUS, signal analysis techniques have been developed to analyze the US signals resulting from the scattering of many unresolved scatterers within the resolution volume of the imaging device. The implementation of these US techniques in PA can enable new applications in biomedicine beyond traditional anatomical PA imaging, further increasing the utilization and impact of this promising 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.044 |
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".