A Frequency-Shift Method to Measure Shear-Wave Attenuation in Soft Tissues
Bibliographic record
Abstract
In vivo quantification of shear-wave attenuation in soft tissues may help to better understand human tissue rheology and lead to new diagnostic strategies. Attenuation is difficult to measure in acoustic radiation force elastography because the shear-wave amplitude decreases due to a combination of diffraction and viscous attenuation. Diffraction correction requires assuming a cylindrical wavefront and an isotropic propagation medium, which may not be the case in some applications. In this paper, the frequency-shift method, used in ultrasound imaging and seismology, was adapted for shear-wave attenuation measurement in elastography. This method is not sensitive to diffraction effects. For a linear frequency dependence of the attenuation, a closed-form relation was obtained between the decrease in the peak frequency of the gamma-distributed wave amplitude spectrum and the attenuation coefficient of the propagation medium. The proposed method was tested against a plane-wave reference method in homogeneous agar-gelatin phantoms with 0%, 10%, and 20% oil concentrations, and hence different attenuations of 0.117, 0.202, and 0.292 [Formula: see text]/Hz, respectively. Applicability to biological tissues was demonstrated with two ex vivo porcine liver samples (0.79 and 1.35 [Formula: see text]/Hz) and an in vivo human muscle, measured along (0.43 [Formula: see text]/Hz) and across (1.77 [Formula: see text]/Hz) the tissue fibers. In all cases, the data supported the assumptions of a gamma-distributed spectrum for the source and linear frequency attenuation for the tissue. This method provides tissue attenuation, which is relevant diagnostic information to model viscosity, in addition to shear-wave velocity used to assess elasticity. Data processing is simple and could be performed automatically in real time for clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".