Structurally enhanced contrast in photoacoustic microscopy with F-Mode imaging
Bibliographic record
Abstract
We present a new technique for photoacoustic (PA) image formation, termed `F-Mode', which capitalizes on variations in the power spectrum of PA signals to produce images with object specific contrast. The technique is applied to a PA dataset by calculating the signal power spectrum at each scan location, segmenting it into discrete frequency bands, and then forming an image representing the spatial power distribution for each band. The appearance of differently sized objects in the resultant F-Mode images is dynamic, and is dictated by the presence of structure specific features in the power spectra. To demonstrate the technique, black polystyrene microspheres with diameters of 6 and 10 μm were scanned using a PA microscope equipped with a 400 MHz transducer and 532 nm laser. The images demonstrated that with appropriate selection of frequency band, visualization of either population of spheres could be selectively enhanced; the 6 μm spheres being more prominent at 249 MHz, while the 10 μm spheres dominated in the 425 MHz F-Mode image. Further, unique frequency dependent patterning in images of individual spheres pointed towards sub-micron diameter fluctuations in spheres from the same population. This proof-of-concept work paves the way for future in vivo applications, such as selectively analyzing blood vessels of different diameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".