Impact of Treatment Trajectory on Temperature Field Uniformity in Biological Tissue Irradiated by Ultrasound Pulses with Shocks
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) treatments typically involve the ablation of tissue volumes comprising multiple focal sites. One aspect of treatment planning involves the definition of a sequence of ultrasound pulses and corresponding focal sites as the sonication trajectory. Here, numerical simulations of the thermal effects of different trajectories are performed for HIFU exposures delivered to an ex vivo bovine liver sample by a clinical array (Sonalleve V2 3.0T system, Profound Medical Corp., Canada). Simulations consider boiling histotripsy regime with millisecond-long pulses that include shocks. Focusing of the ultrasound beam in tissue was modeled by the Westervelt equation, and the temperature field was modeled by the bioheat equation. To explore different treatment strategies, trajectories were considered with discrete foci located along two or four concentric circles with radii from 2 to 8 mm. Two approaches for traversing these focal sites were compared: In the first approach each discrete focus was sonicated by a sequence of 15 pulses before moving to the next site in the trajectory. In the second approach, each focus was sonicated once before moving to the next site, with sonications over the whole trajectory repeated 15 times. The influence of the trajectory’s size and the pulsing strategy on the temperature field was analyzed. It is shown that the structure of the temperature field is more uniform with a longer time interval between repeated irradiation of each focus, and the optimal time interval ranges from three to six pulse repetition periods.
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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 | 0.002 |
| 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) | 0.001 | 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".