Improved frequency-shift method for shear wave attenuation computation
Bibliographic record
Abstract
Ultrasound shear wave elastography and assessment of attenuation can increase the diagnostic accuracy of numerous diseases. The main objective of this work was to propose two improvements to the frequency shift (FS) method for shear wave attenuation assessment. First, the shape parameter of the gamma distribution employed to fit the spectrum amplitude of shear waves now varies spatially. Second, a random sample consensus (RANSAC) line fitting method is utilized for calculating the attenuation due to its superiority in the presence of noise and outliers. The shear wave propagation in a tissue-mimicking numerical phantom was modeled as a Kelvin-Voigt (KV) viscoelastic material with finite element (FE) simulations in COMSOL. The shear wave amplitude spectrum was fit using a gamma distribution function, and the slope of the rate parameter of this function obtained by the RANSAC method provided the attenuation coefficient. This method was applied to two numerical phantoms (viscosity of 0.5 and 2 Pa.s), two experimental tissue-mimicking viscoelastic phantoms, and two ex vivo blood clot samples embedded in phantoms. Numerical phantoms were also investigated in the presence of Gaussian random noise at SNR levels of 20 dB to 0 dB. Results were compared with FS, two-point frequency shift (2P-FS) method, and attenuation measuring ultrasound shear wave elastography (AMUSE) method. Mean values of the attenuation coefficient, averaged over a region of interest, were compared between implemented methods. For simulations at different SNRs, the proposed, 2P-FS and AMUSE methods gave mean values close to the KV model. At a SNR of 0 dB, biases of the proposed method were 0.0025 and 0.0258 Np/m/Hz, and variances were 0.0024 and 0.0158 (Np/m/Hz)2, for viscosities of 0.5 and 2 Pa.s, respectively. For homogeneous gel phantoms, mean values of the proposed method were: #1) 0.4351; #2) 0.4621. For blood clot phantoms, mean values were: #1) 0.2573; #2) 0.9875. Biases of the proposed method compared to KV or AMUSE were smaller for numerical phantoms, whereas its variance was less than 2P-FS for all datasets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".