Cancellous bone characterization using ultrasonic backscatter parametric imaging
Bibliographic record
Abstract
The ultrasonic imaging of the highly attenuated porous media (like spongy bone) is of great interest to the researchers. In this study, we used a 7.5 MHz focal transducer (V321, Panametrics, USA) to scan a total of 35 bovine cancellous bone samples. The −-6dB lateral resolution of the transducer was 0.157 mm which is comparable to the trabecular bone thickness (0.1–0.2mm). Apparent integrated backscatter (AIB) was calculated using different signal of interest (SOI) to reconstruct the ultrasonic backscatter parametric images. The trabecular bone structure can be clearly seen from the images. AIB was positively correlated with bone mineral density (BMD) (R = 0.61, p<0.05), bone volume fraction (BV/TV) (R = 0.53, p<0.05), and apparent bone density (ABD) (R = 0.58, p<0.05) when SOI was chosen around the surface of the bone sample, but negatively correlated with BMD (R=-0.77, p<0.05), BV/TV (R=-0.81, p<0.05), and ABD (R=-0.82, p<0.05) when SOI was chosen below the surface of the bone sample. We suppose that single scattering (SS) and multiple scattering (MS) may lead to this opposite correlation phenomena. When wavelength is comparable to the scatterer diameter, diffractive scattering is complex as predicted by Faran cylinder model. We suggest that MS may play an important role in cancellous bone characterization.
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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 | 0.001 | 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".