Time and frequency-domain biomedical photoacoustic imaging: a comparative study
Bibliographic record
Abstract
In this study, we compare the time-domain (pulsed laser) and frequency-domain (FD) photoacoustic (PA) imaging techniques with respect to their signal-to-noise ratio (SNR), contrast and axial resolution. Experiments are performed using a dual-mode PA system and under the condition of maximum permissible exposure (MPE) for both methods. An analytical model of photoacoustic effect and a Krimholtz-Leedom-Matthaei (KLM) model for employed transducers are developed and used to analyze the experimental results. Experiments reveal that the contrast of the pulsed method suffers from the oscillating baseline and the resolution of the FD-PA is limited by the finite bandwidth as well as combining the in-phase and quadrature signals to generate the envelope signal; both are the requirements to maximize the SNR. It is shown that by increasing the laser power and decreasing the chirp duration within the safety limits, the SNR of the FD-PA method can be enhanced. Also it is demonstrated that the axial resolution of the FD method can be improved by combining its two channels; amplitude and phase. The improved resolution competes with the high resolution generated by pulsed technique.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".