Photothermoacoustic imaging of biological tissues: maximum depth characterization comparison of time and frequency-domain measurements
Bibliographic record
Abstract
The photothermoacoustic (PTA) or photoacoustic (PA) effect induced in light-absorbing materials can be observed either as a transient signal in time domain or as a periodic response to modulated optical excitation. Both techniques can be utilized for creating an image of subsurface light-absorbing structures (chromophores). In biological materials, the optical contrast information can be related to physiological activity and chemical composition of a test specimen. The present study compares experimentally the two PA imaging modalities with respect to the maximum imaging depth achieved in scattering media with optical properties similar to biological tissues. Depth profilometric measurements were carried out using a dual-mode laser system and a set of aqueous light-scattering solutions mimicking photon propagation in tissue. Various detection schemes and signal processing methods were tested to characterize the depth sensitivity of PA measurements. The obtained results demonstrate the capabilities of both techniques and can be used in specific PTA imaging applications for development of image reconstruction algorithms aimed at maximizing system performance. Our results demonstrate that submillimeter-resolution depth-selective PA imaging can be achieved without nanosecond-pulsed laser systems by appropriate modulation of a continuous laser source and a signal processing algorithm adapted to specific parameters of the PA response.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".