Estimating the properties of bone phantom cylinders through the inversion of axially transmitted low-frequency ultrasonic guided waves
Bibliographic record
Abstract
Early detection of osteoporosis has increasingly focused on ultrasonic methods, particularly guided waves in axial transmission to assess cortical bone properties. This study demonstrates the potential of low-frequency measurements (<500 kHz) for accurately inferring cortical mechanical and geometrical properties. A custom ultrasonic transducer centered at 350 kHz was used to acquire data, processed via a 2D fast Fourier transform to obtain dispersion curves. These were compared with simulations generated using the semi-analytical iso-geometric analysis (SAIGA) method, modeling a quasi-cylindrical bone geometry in void or immersed in olive oil. By incorporating an excitability parameter into the inversion algorithm, the proposed method achieved a less than 5% discrepancy between bone phantom properties determined via SAIGA inversion and bulk wave pulse-echo measurements, demonstrating its accuracy and potential for in vivo applications. Results also showed that high-wavenumber modes predominantly reflect material properties, whereas low-wavenumber modes below 100 kHz are sensitive to the overall bone geometry, highlighting the importance of low frequencies for a global bone characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".