Planning of Robotic High Intensity Focused Ultrasound Ablation Via Predictive Models of Thermal Lesions

Abstract

High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technology enabling precise energy delivery for the selective ablation of tumors, while preserving surrounding healthy tissue. Currently, there is no gold standard for defying sonication parameters to cover a tumor surface and volume, as this decision relies solely on the physician’s experience. This work proposes a novel planning algorithm for robotic HIFU procedures that ensure automatic and optimized tumor coverage. The approach relies on a predictive model that estimates the dimensions of HIFU-induced thermal lesions based on sonication parameters (source pressure amplitude and sonication time) and leverages genetic algorithms to compute single lesion placement over the treatment area. The optimization function primarily aims to maximize the surface coverage over a defined target area and then integrates motion planning algorithms. In addition to planar lesions, a volumetric ablation composed by a set of co-planar surface lesions was also evaluated. The method was experimentally validated on ex-vivo tissues through a robotic ultrasound-guided (USg) HIFU platform. This study bridges pre-operative lesion prediction and intra-operative robotic execution, supporting standardized and effective HIFU therapy.