Ultrasound shear wave elastography: numerical modeling and time-frequency analysis with an application in HIFU thermal lesion detection
Bibliographic record
Abstract
In this work, a new numerical framework is proposed and implemented to simulate acoustic wave propagation in 3D viscoelastic heterogeneous media. The framework is based on the elastodynamic wave equation in which a 3D second-order time-domain perfectly matched layer (PML) formulation is developed to model unbounded media. The numerical framework is discretized by a finite difference formulation and its stability analysis is discussed. The proposed numerical method is capable of simulating 3D shear and longitudinal acoustic waves for arbitrary source geometries and excitations, together with arbitrary initial and boundary conditions. After validation of the framework, it was used to simulate the propagation of ultrasound shear wave in high intensity focused ultrasound (HIFU) induced thermal lesions located within soft tissue. The parameters in these simulations were obtained from standard double-indentation measurements of the viscoelastic parameters of normal and thermally coagulated chicken breast tissue samples. A HIFU system was used to induce thermal lesions in tissue. In this study, a new elastography procedure was also introduced to differentiate between the normal and HIFU induced thermal lesions. This method is based on time-frequency analysis of shear wave propagation within the tissue. In the proposed method, the Wigner-Ville distribution has been used as a time-frequency analytical technique to detect the location of shear wave propagating within the tissue, and to estimate the shear speed of the wave as well as its center frequency and attenuation coefficient. This method was applied to the acoustic wave propagation simulation results of the HIFU thermal lesion. It was finally used to estimate the local viscoelastic parameters of the medium. It was demonstrated that the proposed method is capable of differentiating the thermal lesions from the normal tissue based on their viscoelastic parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".