ArticleMedical physics2026
From Skull Density Ratio-based eligibility to efficiency-informed planning: Predictive modeling of thermal response in MR-guided focused ultrasound.
Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTranscranial 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.
methodsWe 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-TER
resultsSDR, 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 R
conclusionsThermal 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.
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