Coregistered photoacoustic and ultrasonic signatures of early bone density variations
Bibliographic record
Abstract
This study examines the application of backscattered ultrasound (US) and photoacoustic (PA) signals for assessment of bone structure and density variations. Both methods were applied in the frequency-domain, employing linear frequency modulation chirps. A near-IR laser (800 nm) was used for inducing the PA signal. The backscattered pressure waves were detected with a 2.2-MHz US transducer. Experiments were focused on detection and evaluation of PA and US signals from in-vitro animal and human bones with cortical and trabecular sublayers. It was shown that PA signals can be detected as deep as a few millimeters below trabecular and cortical layers. The occurrence of multiple scattering was demonstrated in PA detected signals from cancellous bone. Osteoporotic changes in the bone were simulated by using a very mild demineralization ethylenediaminetetraacetic acid solution. Changes in the time-domain signals as well as integrated backscattering spectra were compared for the samples before and after demineralization. The results demonstrated the sensitivity of PA to variations in bone minerals. In comparison to PA, US was capable of generating detectable signals from deeper bone sublayers (few centimeters). However, while US signal variations with changes in the cortical layer were insignificant, PA proved to be sensitive even to minor variations of the cortical bone density.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".