Ultrasound spectral analysis of photoacoustic signals from red blood cell populations at different optical wavelengths
Bibliographic record
Abstract
Spectral analysis of photoacoustic (PA) signals in the ultrasound frequency domain is a method that analyzes the power spectrum of PA signals to quantify tissue microstructures. PA spectral analysis has been correlated to changes in the size, morphology and concentration of absorbers that are smaller than the system spatial resolution. However, the calculated spectral parameters are still not system independent due to difficulty in eliminating variations in the light distribution for different optical wavelengths. Changes in spectral parameters for the same absorber geometry but different optical illumination wavelengths needs to be carefully examined. A gelatin vessel phantom is used. The vessels contain red blood cells comprised of oxy, deoxy and methemoglobin induced using oxygen, sodium hydrosulfite and sodium nitrite, respectively. The samples were imaged using the VevoLAZR system at wavelengths 680 – 905 nm in steps of 15 nm. The radiofrequency (RF) signals were analyzed to calculate the spectral slope. The results were compared to simulated RF signals acquired using the mcxyz Monte Carlo package coupled to the solution of the PA wave equation using the Green’s function approach. Changes in the spectral slope as a function of optical wavelength were detected. For longer optical wavelengths, the spectral slope increased for deoxyhemoglobin, but decreased for oxyhemoglobin and methemoglobin. The changes in the spectral slope were correlated to changes in the fluence distribution as optical properties change for different wavelengths. The change in the spectral slope as a function of optical wavelength and chromophore content can potentially be used in spectral unmixing for better estimation of hemoglobin content.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".