Three-layer model with absorption for conservative estimation of the maximum acoustic transmission coefficient through the human skull for transcranial ultrasound stimulation
Bibliographic record
Abstract
Transcranial ultrasound stimulation (TUS) has been shown to be a safe and effective technique for non-invasive superficial and deep brain stimulation. Safe and efficient translation to humans requires estimating the acoustic attenuation of the human skull. Nevertheless, there are no international guidelines for estimating the impact of the skull bone. A tissue independent, arbitrary derating was developed by the U.S. Food and Drug Administration to take into account tissue absorption (0.3 dB/cm-MHz) for diagnostic ultrasound. However, for the case of transcranial ultrasound imaging, the FDA model does not take into account the insertion loss induced by the skull bone, nor the absorption by brain tissue. Therefore, the estimated absorption is overly conservative which could potentially limit TUS applications if the same guidelines were to be adopted. Here we propose a three-layer model including bone absorption to calculate the maximum pressure transmission through the human skull for frequencies ranging between 100 kHz and 1.5 MHz. The calculated pressure transmission decreases with the frequency and the thickness of the bone, with peaks for each thickness corresponding to a multiple of half the wavelength. The 95th percentile maximum transmission was calculated over the accessible surface of 20 human skulls for 12 typical diameters of the ultrasound beam on the skull surface, and varies between 40% and 78%. To facilitate the safe adjustment of the acoustic pressure for short ultrasound pulses, such as transcranial imaging or transcranial ultrasound stimulation, a table summarizes the maximum pressure transmission for each ultrasound beam diameter and each frequency.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".