The impact of the central opening on nonlinear effects in ultrasound fields generated by Sonalleve V1 and V2 MR-HIFU systems
Bibliographic record
Abstract
Previous studies have shown that the Sonalleve MR-HIFU clinical system can generate shocks of 80–105 MPa at the focus, which is sufficient for boiling histotripsy. Two versions of the system (V1 and V2) have therapeutic transducers that differ in focusing angle, arrangement of elements, and the size of a central opening. The goal here was to reveal the impact of different array geometries on shock amplitudes at the focus. Nonlinear modeling of the array’s field in water using boundary conditions reconstructed from holography shows that at the same power output, the V2 array generates 10–15 MPa lower shock amplitudes at the focus. Although this difference is mainly caused by the smaller focusing angle of the V2 array, the larger central opening of the V2 array has a nontrivial impact. By suppressing a direct wave, the central opening produces a somewhat higher shock amplitude. Axisymmetric equivalent source models were constructed for both arrays and the importance of including the central opening was demonstrated. These models can be used in the “HIFU beam” software for simulating nonlinear fields of the Sonalleve V1 and V2 systems in water and flat-layered biological tissues. [Work supported by RSF-22-72-00047 and NIH R01EB025187.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".