Regularized phantom-free construction of local attenuation coefficient slope maps for quantitative ultrasound imaging
Bibliographic record
Abstract
Constructing an attenuation map based on local attenuation coefficient slope (ACS) in quantitative ultrasound (QUS) has shown potential in the diagnosis of liver steatosis. Detecting tumors in the liver and differentiating abnormalities in tissues are some other applications for these maps. In this work, we considered the construction of semantic parametric maps and a recent method providing a phantom-free estimation of local ACS. The main goal was to propose a methodology for constructing regularized phantom-free local ACS maps based on this framework. The proposed method was tested on two tissue mimicking (#1 and #2) with different attenuation: i) homogeneous phantoms; and ii) side-by-side phantoms. Modifications brought to previous works include: a) a linear interpolation of the power spectrum in log-scale; b) the relaxation of the underlying hypothesis on the diffraction factor; c) a generalization to nonhomogeneous local ACS; and d) an adaptive restriction of frequencies to a more reliable range for this purpose than the usable frequency range. The regularization was formulated as generalized LASSO, and a variant of the Bayesian Information Criterion (BIC) was applied to estimate the Lagrangian multiplier on the LASSO constraint. Ultrasound acquisitions were performed with a Verasonics Vantage 256 scanner (Redmond, WA) using an ATL L7-4 probe (Philips, Bothell, WA) driven at 5 MHz, using 21 angles (-5° to 5°) compounding. In this work, power spectra were averaged over 25 scanlines, each spanning 10 pulse lengths. i) On homogeneous phantoms, normalized root mean squared errors (NRMSE) were (in %): #1) 8.9 ± 3.6; #2) 7.0 ± 1.6. Without regularization, the phantom-free method yielded NRMSEs of: #1) 19.1 ± 2.1; #2) 11.5 ± 1.0. ii) On phantoms with side-by-side media, NRMSEs were: #1) 32.3 ± 8.5; #2) 10.3 ± 6.8. Without regularization, NRMSEs were #1) 35.1 ± 8.7; #2) 13.2 ± 5.2. The contrast-to-noise-ratio was 4.4 ± 1.7 (no units) with regularization, and 2.6 ± 0.6 without it. Future work should address the problem of boundaries between distinct media (tissues).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".