Photoacoustic tissue characterization using envelope statistics and ultrasonic spectral parameters
Bibliographic record
Abstract
Photoacoustic (PA) tissue characterization relies on the analysis of ultrasound (US) signals generated through the PA effect. The probability distributions of PA signal amplitude as well as the frequency content of the PA signals are typically not considered. We present a phantom study where we introduce the combined use of US/PA signal envelope statistics along with analysis of the frequency content of the US/PA signals for the purposes of monitoring physical changes in the absorbers. The phantoms were constructed using black polystyrene beads (radius 1.77 μm and 7.36 μm). Tissue microenvironment was emulated by homogenously mixing 10 beads/imaging-transducer-resolution-volume in order to accommodate large numbers of sub-resolved beads. The phantoms were imaged with the Vevo LAZR US/PA integrated imaging system (Fujifilm-VisualSonics) using a 40 MHz linear array probe and 680 nm illumination. US/PA signals from the same region of interest were analyzed by mapping the signal amplitudes distributions from 5 phantom locations, fitting the data to the Generalized Gamma (GG) distribution and extracting the fit parameters while computing the normalized power spectra to retrieve the spectral slope (SS) and midband fit (MBF) spectral parameters. The GG scale parameter increased by 500x for US images and 12x for PA equivalents as the size of the beads increased by ~4.5x. The SS decreased by 0.8x for US and 0.4x for PA. These changes can be attributed variations in size and spatial organization of the beads suggesting that combined US/PA statistical and spectral analysis can potentially monitor normal and abnormal tissue physiological changes.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".