Fluence-matching method based on photoacoustic radiofrequency spectraTR (Conference Presentation)
Bibliographic record
Abstract
Photoacoustic (PA) signals carry information of the absorbing chromophores and the light distribution in imaged samples. The dependence of light distribution with optical wavelength affects the accuracy in PA chromophore quantification. Oxygen saturation (sO2) estimations maybe inaccurate in-depth due to the lack of proper fluence compensation. We propose the use of the PA radiofrequency spectral slope (SS) to generate a frequency filter to match the fluence across optical wavelengths. The SS is calculated from the ratio of the radiofrequency power spectra at the selected optical wavelengths. The SS relays information about the absorbers’ size and the light distribution. At the imaged optical wavelengths of the same sample, the SS-estimated size should in principle remain unchanged. This suggests that any changes in the measured SS as a function of optical wavelength can be attributed to the light distribution. A frequency filter can be designed from the computed SS and applied to compensate the PA images. A 5mm phantom consisting of fresh blood, intralipid and gelatin was imaged using the VevoLAZR system at 750 and 850nm. A square sliding window sized 1.6mm with 80% overlap is applied to segment the generated radiofrequency signals. The designed ultrasound filter was applied to each segmented signal. As a result, the fluence-induced depth fluctuations in the sO2 estimations dropped from 9.49%/mm to 1.83%/mm. This will allow for more accurate sO2 estimates that are less depth dependent. The approach provides a new perspective for fluence compensation which can aid in improving chromophore quantification using PA imaging.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".