Quantification of the backscatter coefficient in heterogeneous media: Comparison of attenuation compensation methods
Bibliographic record
Abstract
The estimation of backscatter coefficient, from in vivo tissue requires accurate compensation for intervening tissue attenuation. Current attenuation compensation methods (ACM) have been tested in fully or piecewise homogeneous phantoms. Thus, evidence of their performance in complex media remains scant. In this study we compare the performance of two ACM in a tissue-mimicking material with strong reflectors (SR). A gel-based phantom with SR was scanned with a L11-5v transducer on a Vantage 128 system (Verasonics). Estimates of from the phantom's background were obtained from the IQ echo signals using the known phantom attenuation (KA) or two ACM (a Constrained-Log-Difference (CLD) and a dynamic programming (DP) regularized method).The former method was used as gold standard. To analyze bias versus depth, a Pearson correlation coefficient was computed and the mean difference (MD) vs. depth was estimated. CLD-based showed a strong correlation (r = 0.96) with depth, while the correlation of DP-based estimates was weaker. DP-based values were closer to those obtained with KA (MD [0.9–1.15]) compared to the CLD-based estimates (MD [1.0–1.79]). The DP, regularized ACM show less sensitivity to the presence of SR compared to conventional ACM. We are currently using DP-based ACM to characterize breast lesions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".