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Updated: Jul 15, 2025
Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System
Published on: March 6, 2019
Artificial intelligence-assisted ultrasound-guided focused ultrasound therapy: a feasibility study
Moslem Sadeghi-Goughari1, Hossein Rajabzadeh1, Jeong-Woo Han1
1Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Objectives:
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.
Methods:
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.
Results:
Our results showed a 93% accuracy in identifying ablated areas marked by the hyperechogenicity effect in B-mode imaging.
Conclusion:
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.
