Comparison of different attenuation compensation methods in the estimation of the ultrasound backscatter coefficient
Bibliographic record
Abstract
This study applied three methods to compensate for intervening tissue attenuation in the estimation of the backscatter coefficient of a tissue-mimicking phantom with spatial variations of attenuation. The first method, considered as the gold standard, made use of attenuation estimates measured using an independent substitution technique. The second method estimated first the local attenuation using a Constrained-Spectral Log Difference (CLD) technique, and then used these values to quantify the total intervening tissue attenuation. The third method consisted on a regularized, dynamic programming technique that simultaneously computes the backscatter coefficient and the total attenuation. Two variants of the latter method were investigated, one with search ranges centered around the expected attenuation and backscatter values of the phantoms, and a second one with search reaches expanded to include attenuation and backscatter values expected in different breast tissues. Our results indicate that, although the CLD method provided more precise estimates of the local attenuation, the DP method with search ranges centered around the expected phantom values provided more accurate attenuation compensation and backscatter coefficient estimation. When increasing the search ranges, the accuracy and precision of DP was importantly reduced.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".