Robust guided wave inversion for estimating bone thickness and elasticity
Bibliographic record
Abstract
Accurately characterizing bone properties using quantitative ultrasound remains a significant challenge due to the dispersive nature of guided waves, limited observations, irregularity of bone structure, and heterogeneity of bone tissues. In this paper, an inversion technique is proposed that combines weighted mean absolute criteria and the simulated annealing algorithm to extract the thicknesses and elastic properties of a bilayer bone model. By utilizing the L1 norm with an appropriate weighting parameter, this method effectively reduces the influence of outliers and noises commonly encountered in ultrasonic data, leading to more accurate estimation. This paper also introduces an asymptotic scheme to significantly reduce the search domain, improving the speed and precision of the inversion process. This approach employs a spectral collocation method as a forward modeling technique to simulate guided waves in a bone plate coated by a soft tissue layer. This paper validates the inversion using simulated and ex vivo data and demonstrates its ability to estimate features of cortical bone and soft tissue with high accuracy. Results are presented for the isotropic model. These findings hold great promise for the accurate characterization of bone properties using quantitative ultrasound, with potential applications in clinical diagnosis and treatment of bone-related diseases and injuries.
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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 | 0.002 |
| 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 | 0.001 |
| 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".