Spectral ratio method to estimate broadband ultrasound attenuation of cortical bones<i>in vitro</i>using multiple reflections
Bibliographic record
Abstract
Broadband ultrasound attenuation (BUA) is commonly measured by the spectral ratio method. Conventionally BUA is measured in transverse transmission mode where ultrasound signal is recorded with and without the sample. The spectral ratio method was extended to estimate nBUA (BUA normalized by thickness) in axial transmission mode using spectral amplitudes of the primary reflection and multiple reflection, which echoes more than once between the material interfaces within a layer. We performed three experiments. First, reflections were numerically simulated to verify the accuracy of the method. We then applied the method to estimate attenuation of silicon rubber and the cortex of a bovine femur. The center frequency of the transducers is 2.25 MHz. We obtained 93% accuracy for a simulated data set with 10% random noise after bandpass filtering. For the silicon rubber, 15 measurements were collected and the mean attenuation was 6.33 +/- 0.19 dB MHz(-1) cm(-1). For the bovine bone, eight measurements were performed in the middle portion of the femur. The mean attenuation was 4.91 +/- 0.65 dB MHz(-1) cm(-1) and compared well with those reported in the literature. The results demonstrate that the proposed method has the potential to provide a quick, reliable and robust cortical attenuation assessment in vivo.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".