The application of sparse arrays in high frequency transcranial focused ultrasound therapy: A simulation study
Bibliographic record
Abstract
PURPOSE: Transcranial focused ultrasound is an emerging therapeutic modality that can be used to perform noninvasive neurosurgical procedures. The current clinical transcranial phased array operates at 650 kHz, however the development of a higher frequency array would enable more precision, while reducing the risk of standing waves. However, the smaller wavelength and the skull's increased distortion at this frequency are problematic. It would require an order of magnitude more elements to create such an array. Random sparse arrays enable steering of a therapeutic array with fewer elements. However, the tradeoffs inherent in the use of sparsity in a transcranial phased array have not been systematically investigated and so the objective of this simulation study is to investigate the effect of sparsity on transcranial arrays at a frequency of 1.5 MHz that provides small focal spots for precise exposure control. METHODS: Transcranial sonication simulations were conducted using a multilayer Rayleigh-Sommerfeld propagation model. Element size and element population were varied and the phased array's ability to steer was assessed. RESULTS: The focal pressures decreased proportionally as elements were removed. However, off-focus hotspots were generated if a high degree of steering was attempted with very sparse arrays. A phased array consisting of 1588 elements 3 mm in size, a 10% population, was appropriate for steering up to 4 cm in all directions. However, a higher element population would be required if near-skull sonication is desired. CONCLUSIONS: This study demonstrated that the development of a sparse, hemispherical array at 1.5 MHz could enable more precision in therapies that utilize lower intensity sonications.
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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 | 0.001 | 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".