Reza Haqshenas | Searcharxiv

arXiv2026

Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Derived Heterogeneous Skull Data

Transcranial ultrasound therapy uses focused acoustic energy to induce therapeutic bioeffects in the brain. Ultrasound must be transmitted through the skull, which is highly attenuating and heterogeneous, causing beam distortion, reducing focal pressure, and shifting the target location. Computational models are frequently used to predict beam aberration, assess cranial heating, and correct the phase of ultrasound transducers. These models often rely on computed tomography (CT) images to build patient-specific geometries and estimate skull acoustic properties. However, the coarse voxel resolution of CT limits accuracy for differential equation solvers at ultrasound frequencies. This paper presents an efficient numerical method based on volume-surface integral equations to model full-wave acoustic propagation through heterogeneous skull bone. We show that our approach effectively simulates transcranial ultrasound, even when using the original CT voxels as the computational mesh, where the 0.5 mm voxel length is relatively coarse compared to the shortest wavelength of 3 mm. The method is validated against a high-resolution boundary element model using an averaged skull representation. Simulations using a CT-based skull model and a bowl transducer reveal significant beam distortion of 7.8 mm attributed to the skull's heterogeneous acoustical properties.

physics.med-ph↗

arXiv2025

Classifying acoustic cavitation with machine learning trained on multiple physical models

Acoustic cavitation threshold charts are used to map between acoustic parameters (mainly intensity and frequency) and different regimes of acoustic cavitation. The two main regimes are transient cavitation, where a bubble collapses, and stable cavitation, where a bubble undergoes periodic oscillations without collapse. The cavitation charts strongly depend on the physical model used to compute the bubble dynamics and the algorithm for classifying the cavitation threshold. The differences between modeling approaches become especially noticeable for resonant bubbles and when sonication parameters result in large-amplitude oscillations. This paper proposes a machine learning approach that integrates three physical models, i.e., the Rayleigh-Plesset, Keller-Miksis and Gilmore equations, and multiple cavitation classification techniques. Specifically, we classify the cavitation regimes based on the maximum radius, the acoustic Mach number, the kurtosis factor of acoustic emissions, and the Flynn criterion on the inertial and pressure functions. Four machine learning strategies were developed to predict the likelihood of the transient and stable cavitation, using equally weighted contributions from classification techniques. By solving the differential equations for bubble dynamics across a range of sonication and material parameters and applying cross-validation on held-out test data, our framework demonstrates high predictive accuracy for cavitation regimes. This physics-informed machine learning approach offers probabilistic insights into cavitation likelihood, combining diverse physical models and classification strategies, each contributing different levels of physical rigor and interpretability.

physics.comp-ph↗