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Focused ultrasound (FUS) therapy has emerged as a promising noninvasive solution for tumor ablation. Accurate monitoring and guidance of ultrasound energy is crucial for effective FUS treatment. Although ultrasound (US) imaging is a well-suited modality for FUS monitoring, US-guided FUS (USgFUS) faces challenges in achieving precise monitoring, leading to unpredictable ablation shapes and a lack of quantitative monitoring. The demand for precise FUS monitoring heightens when complete tumor ablation involves controlling multiple sonication procedures. To address these challenges, we propose an artificial intelligence (AI)-assisted USgFUS framework, incorporating an AI segmentation model with B-mode ultrasound imaging. This method labels the ablated regions distinguished by the hyperechogenicity effect, potentially bolstering FUS guidance. We evaluated our proposed method using the Swin-Unet AI architecture, conducting experiments with a USgFUS setup on chicken breast tissue. Our results showed a 93% accuracy in identifying ablated areas marked by the hyperechogenicity effect in B-mode imaging. Our findings suggest that AI-assisted ultrasound monitoring can significantly improve the precision and control of FUS treatments, suggesting a crucial advancement toward the development of more effective FUS treatment strategies.

Artificial intelligence-assisted ultrasound-guided focused ultrasound therapy: a feasibility study