Photoacoustic imaging using lock-in amplification and pulsed fiber lasers
Bibliographic record
Abstract
Photoacoustic (PA) imaging is a non-invasive, non-ionizing imaging technology with high optical contrast between blood and tissue, and with high sensitivity of hemoglobin concentration and oxygen saturation due to different optical absorption spectra resulting from different oxygenation of hemoglobin. Most PA imaging systems implement a nanosecond pulsed laser source as excitation source to induce PA signal, and rely on broadband amplifiers to record time-domain PA signals [1-6]. Some groups, however, have reported using modulated continuous-wave lasers as an excitation source for frequency-domain imaging [7-9]. Frequency-domain imaging offers the potential of lock-in amplification which has sensitivities as low as nV even in noise orders of magnitude higher than the signal. However, although modulated CW sources works for low cost and compact PA imaging, it does not satisfy thermal and stress confinement conditions required for optimal PA signal strength. Here, we investigate a PA methodology using pulsed fiber lasers as excitation laser source combined with lock-in amplification technology. For comparison, we also studied time-domain PA methodology. Phantom studies show that signal-to-noise ratio (SNR) obtained with frequency domain PA imaging is significantly more sensitive than that obtained using time-domain PA imaging when the laser pulse repetition rate (PRR) matches the bandwidth of ultrasound transducer. Therefore, high sensitive PA imaging technology using pulsed fiber laser sources with lock-in amplification may potentially greatly extend the depth of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".