The Use of Focused Ultrasound Beams with Shocks to Suppress Diffusion Effects in Volumetric Thermal Ablation of Biological Tissue
Bibliographic record
Abstract
The article presents the results of numerical simulation of an experiment on irradiating ex vivo bovine liver sample by the therapeutic array of the MR-HIFU clinical system (Sonalleve V1 3.0T, Profound Medical Corp., Canada). Continuous quasi-linear and pulsed shock-wave exposures with the same time-averaged power are compared. Volumetric thermal lesions were generated by moving the focus of the array in its focal plane along discrete trajectories consisting of two or four concentric circles with a maximum radius of 4 mm. The effect of using the criteria for controlling the thermal dose during treatment and ending the sonication on the shape, volume, and exposure time of generating thermal lesion were analyzed. The acoustic field in tissue was calculated using the Westervelt equation; the temperature field was simulated with the inhomogeneous heat conduction equation; and the lesion boundary was determined according to the thermal dose threshold. In the quasi-linear mode corresponding to the clinical one, thermal diffusion leads to elongation of the lesion by a factor of 2–3 along the beam axis compared to the transverse dimension of the trajectory. The use of pulsed shock-wave exposures with switching off the inner circles of the trajectory as they reach the threshold value of the thermal dose makes it possible to significantly suppress the thermal diffusion effects in the axial direction of the beam and obtain localized thermal lesion of a given shape with a thermal ablation rate comparable to the clinical case.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".