Comparison between pulsed laser and frequency-domain photoacoustic modalities: Signal-to-noise ratio, contrast, resolution, and maximum depth detectivity
Bibliographic record
Abstract
In this work, a detailed theoretical and experimental comparison between various key parameters of the pulsed and frequency-domain (FD) photoacoustic (PA) imaging modalities is developed. The signal-to-noise ratios (SNRs) of these methods are theoretically calculated in terms of transducer bandwidth, PA signal generation physics, and laser pulse or chirp parameters. Large differences between maximum (peak) SNRs were predicted. However, it is shown that in practice the SNR differences are much smaller. Typical experimental SNRs were 23.2 dB and 26.1 dB for FD-PA and time-domain (TD)-PA peak responses, respectively, from a subsurface black absorber. The SNR of the pulsed PA can be significantly improved with proper high-pass filtering of the signal, which minimizes but does not eliminate baseline oscillations. On the other hand, the SNR of the FD method can be enhanced substantially by increasing laser power and decreasing chirp duration (exposure) correspondingly, so as to remain within the maximum permissible exposure guidelines. The SNR crossover chirp duration is calculated as a function of transducer bandwidth and the conditions yielding higher SNR for the FD mode are established. Furthermore, it was demonstrated that the FD axial resolution is affected by both signal amplitude and limited chirp bandwidth. The axial resolution of the pulse is, in principle, superior due to its larger bandwidth; however, the bipolar shape of the signal is a drawback in this regard. Along with the absence of baseline oscillation in cross-correlation FD-PA, the FD phase signal can be combined with the amplitude signal to yield better axial resolution than pulsed PA, and without artifacts. The contrast of both methods is compared both in depth-wise (delay-time) and fixed delay time images. It was shown that the FD method possesses higher contrast, even after contrast enhancement of the pulsed response through filtering.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".