Poster Poster Program Therapy Physics

High-Resolution CT–Guided Multiparametric Navigation of Transcranial Focused Ultrasound Therapy

Abstract
Purpose

Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive neuromodulation modality, offering unparalleled focal precision and depth penetration. Accurate treatment planning critically depends on the ability to predict acoustic propagation through the skull, commonly inferred from Hounsfield units (HU) in computed tomography (CT) imaging. High-resolution Photon-counting CT (PCCT) images, by substantially reducing skull-related imaging artifacts and enhancing quantitative fidelity, enables more reliable HU-based prediction of treatment outcomes.

Methods

Head PCCT data with a native spacing of (0.68, 0.68, 0.7) was reconstructed into a 3D volume of (1, 1, 1). Acoustic parameters—including sound speed, density, and attenuation—were subsequently derived from the corresponding HU. A skull-specific acoustic attenuation framework was implemented using the k-Wave toolbox in MATLAB, and the focal search region was determined via a radius-positioned algorithm. Simulations across 100–750 kHz, sampled at 50 kHz intervals, were conducted to validate the predicted acoustic field distribution.

Results

Approximately 35.71% of the focal trajectories favored orientations perpendicular to the skull, predominantly within the 400–550 kHz range. Regions of higher skull density ratio (SDR) were associated with greater acoustic gain. Notably, the skull thickness (ST) along the optimal propagation path was roughly 60% of the maximum cranial thickness. Incorporating SDR and ST optimization strategies reduced computational time by approximately 83.65%.

Conclusion

Multiparametric PCCT imaging enhances the discrimination between bone and soft tissue, enabling more accurate three-dimensional skull reconstruction. Despite these advances, PCCT-guided tFUS still requires individualized trajectory planning to achieve maximal therapeutic efficacy. Notably, Algorithmic improvements further substantially accelerate computation, facilitating more efficient treatment planning.

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