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Objective Accurate acoustic modelling of the skull is important for simulation-guided transcranial focused ultrasound (tFUS) applications. However, existing skull parameterization strategies vary widely in complexity and reported accuracy. This study systematically evaluates commonly used modelling approaches to determine their predictive performance across multiple frequencies and skull types. Approach Five skull modelling strategies were assessed: two voxel-wise linear mapping models, one with fixed attenuation coefficient and one with Hounsfield unit (HU)-dependent attenuation coefficients; two three-layer models with fixed and skull-density ratio (SDR) dependent attenuation coefficient; and one single-layer fixed-parameter model. Nineteen regions of interest from five historical and two Thiel-embalmed human skulls were examined at 220 kHz, 680 kHz and 1000 kHz, bowl-surface source fields were reconstructed using holography, and intracranial pressure distributions were captured with a needle hydrophone. Measurements were compared with k-Wave simulations. Results Average simulated peak pressure errors (ɛp) ranged from 20% to 31% across frequencies, whereas intensity errors (ɛI) reached 41% to 81%. Errors in -6 dB focal volume ranged from 18% to 65%, and focal position discrepancies were typically several millimeters. Simulation generally predicted smaller insertion losses than measured, indicating a tendency to underestimate skull-related attenuation and overestimate transmitted intracranial exposure. Among the five modelling strategies, the model with the lowest observed error varied with frequency and outcome. Paired specimen-level comparisons did not establish a consistent accuracy advantage for any strategy. Significance These results indicate that current skull modelling strategies may reproduce the gross intracranial beam pattern while still misestimating the delivered acoustic exposure, focal coverage and localization. Such errors are directly relevant to tFUS dose selection, target coverage and safety-margin interpretation, underscoring the need for refined parameterization methods for reliable tFUS treatment planning.
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Li et al. (2026) studied this question.
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