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

Autor: Moslem Sadeghi-Goughari, Hossein Rajabzadeh, Jeong-woo Han, Hyock-Ju Kwon
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Hyperthermia, Vol 40, Iss 1 (2023)
Druh dokumentu: article
ISSN: 02656736
1464-5157
0265-6736
DOI: 10.1080/02656736.2023.2260127
Popis: AbstractObjectives 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje