From Skull Density Ratio-based eligibility to efficiency-informed planning: Predictive modeling of thermal response in MR-guided focused ultrasound.

BACKGROUND: Transcranial MR-guided focused ultrasound (MRgFUS) enables incisionless thermoablation of deep brain targets and has become an established modality in functional neurosurgery. Treatment efficiency is strongly influenced by skull-mediated ultrasound attenuation. Although the skull density ratio (SDR) is currently the primary parameter used for pre-procedural screening, it provides only a partial description of skull-related variability in thermal response and offers limited guidance for intra-procedural energy titration. PURPOSE: To comprehensively characterize skull-related determinants of thermal efficiency beyond SDR and to develop multivariate models for predicting treatment efficiency and sonication-wise peak temperature to support patient screening and intra-procedural decision-making. METHODS: We retrospectively analyzed 316 MRgFUS thermoablative procedures (214 thalamotomies and 102 subthalamotomies). Skull metrics derived from CT included SDR, skull thickness (ST), diploe thickness (DT), angle of incidence (AOI), and higher-order distributional descriptors. Thermal efficiency was quantified using multiple temperature-energy-based estimators. Two predictive models were developed, each validated on an independent held-out test set (20% of procedures): (1) a forward-selected multivariate linear regression model for procedure-level thermal efficiency (temperature-to-energy ratio at 55°C-TER55), guided by adjusted R2 and AIC, with performance reported as R2; and (2) a gradient boosting model for sonication-wise peak temperature prediction, integrating sonication parameters, skull metrics, and cumulative treatment history, with performance reported as MAE globally and stratified by temperature range. RESULTS: SDR, ST, and DT were significantly associated with thermal efficiency, and composite metrics such as SDR/DT outperformed SDR alone. A five-feature multivariable linear regression model based on skull-derived metrics achieved an adjusted R2 of 0.63 (cross-validated R2 = 0.61, MAE = 0.54°C/kJ) for predicting TER55, with consistent generalization on an independent held-out test set (R2 = 0.58, MAE = 0.53°C/kJ). The peak temperature prediction model achieved a cross-validated MAE of 1.70°C on the training set and 1.91°C on a fully held-out procedure-level test set, with stable performance across clinically dominant temperature ranges (< 60°C). Prediction accuracy was reduced at ≥60°C, reflecting data imbalance and systematic underestimation at extreme temperatures. CONCLUSIONS: Thermal efficiency in MRgFUS cannot be adequately characterized by SDR alone. A multivariate efficiency model integrating five skull descriptors substantially outperforms SDR-based screening, providing a more informative and continuous representation of treatment feasibility and enabling pre-procedural estimation of expected energy requirements. When combined with treatment parameters and cumulative thermal history, multivariate models enable accurate sonication-wise temperature prediction, supporting efficiency-informed energy titration and dynamic procedural guidance, and advancing MRgFUS toward more precise and personalized therapy.