Local Harmonic Motion Monitoring of Focused Ultrasound Surgery—A Simulation Model
Bibliographic record
Abstract
In this paper, a computational model for localized harmonic motion (LHM) imaging-based monitoring of high-intensity focused ultrasound surgery (FUS) is presented. The LHM technique is based on a focused, time-varying ultrasound radiation force excitation, which induces local oscillatory motions at the focal region. These vibrations are tracked, using pulse-echo imaging, and then, used to estimate the mechanical properties of the sonication region. LHM is feasible for FUS monitoring because changes in the material properties during the coagulation process affect the measured displacements. The presented model includes separate models to simulate acoustic sonication fields, sonication-induced temperature elevation and mechanical motion, and pulse-echo imaging of the induced motions. These 3-D simulation models are based on Rayleigh-Sommerfield integral, finite element, and spatial impulse response methods. Simulated-tissue temperature elevation and mechanical motion were compared with previously published in vivo measurements. Finally, the simulation model was used to simulate coagulation and LHM monitoring, as would occur with multiple, neighbouring sonication locations covering a large tumor.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".