Laser Photothermoacoustic Heterodyned Lock-in Depth Profilometry in Turbid Tissue Phantoms
Bibliographic record
Abstract
Frequency-domain correlation and spectral analysis photothermoacoustic FD-PTA imaging is a promisingnew technique, which is being developed to detect tumor masses in turbid biological tissue. Unlike conventional biomedical photoacoustics which uses time-of-flight acoustic information induced by a pulsed laser to indicate the tumor size and location, in this research, a new FD-PTA instrument featuring frequency sweep chirp and heterodyne modulation and lock-in detection of a continuous-wave laser source at 1064 nm wavelength is constructed and tested for its depth profilometric capabilities with regard to turbid media imaging. Owing to the linear relationship between the depth of acoustic signal generation and the delay time of signal arrival to the transducer, information specific to a particular depth can be associated with a particular frequency in the chirp signal. Scanning laser-fluence modulation frequencies with a linear frequency sweep method preserves the depth-to-delay time linearity and recovers FD-PTA signals from a range of depths. Combining with the depth information carried by the back-propagated acoustic chirp signal at each scanning position, one could rapidly generate subsurface three-dimensional images of the scanning area at optimal signal-to-noise ratios and low laser fluences, a combination of tasks that is difficult or impossible by use of pulsed photoacoustic detection. In this paper, results of PTA scans performed on tissue mimicking control phantoms with various optical, acoustical, and geometrical properties are presented. A mathematical model is developed to study the laser-induced photothermoacoustic waves in turbid media. The model includes both the scattering and absorption properties of the turbid medium. A good agreement is obtained between the experimental and numerical results. It is concluded that frequency domain photothermoacoustics using a linear frequency sweep method and heterodyne lock-in detection has the potential to be a reliable tool for biomedical depth-profilometric 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".