Towards transcranial focused ultrasound treatment planning: A technique for reduction of outer skull and skull base heating in transcranial focused ultrasound
Bibliographic record
Abstract
Transcranial focused ultrasound is a rapidly-growing therapeutic modality with expanding applications in the treatment of brain disorders and diseases. As more treatments are proposed by clinicians, the need for comprehensive, accurate treatment planning is required to take into account the complexities that can arise from the application of ultrasound to the brain. These include skull heating, both on the outer surface of the skull and at any bone at the skull base, as well as phasing corrections for skull aberration corrections. We will present a method for the reduction of outer skull and skull base heating by using phased array controls. First, full-wave numerical simulations are used to demonstrate the corrections on a clinically relevant skull base target using exported clinical imaging data. Then, results from ex vivo experiments are presented to illustrate the application of these phased array controls in the reduction of skull heating by scanning a 3D volume of heating around the focus, while computing the corrections in a clinically relevant timescale. Potential limitations of the method and future directions will also be discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".