Ultrasound-modulated optical imaging using a confocal Fabry-Perot interferometer and a powerful long pulse laser
Bibliographic record
Abstract
Ultrasound-modulated optical imaging combines the good spatial resolution of ultrasonic waves (mm scale) and the spectroscopic properties of light to detect optically absorbing objects inside thick (cm scale) highly scattering media. Light propagating in a scattering medium can interact with an ultrasonic wave thereby being tagged by a frequency shift equal to the ultrasound frequency or its harmonics. In this paper, a confocal Fabry-Perot interferometer (CFPI) is used as a tunable spectral filter to detect selectively the ultrasound-tagged photons. The CFPI allows obtaining high spectral resolution (MHz scale) while maintaining a high light gathering power when compared to other spectroscopic devices of comparable resolution. The contrast between the tagged photons and the untagged photons can be further enhanced by cascading CFPI. Moreover, the fast response of the CFPI allows performing measurements within the speckle decorrelation time typically encountered in biomedical applications. In this paper, the use of a single-frequency laser emitting powerful optical pulses allows illuminating the scattering medium only during the transit time of the probing ultrasonic pulses. Consequently, the acoustic and the optical power are both concentrated in time to enhance the signal-to- noise ratio of the technique while remaining below the biomedical safety limits. The detection of optically absorbing objects (mm size) inside 30- and 60-mm thick scattering media is presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".