High-intensity focused ultrasound (HIFU) is considered as an important non-invasive way for tumor ablation in deep organs. However, accurate real-time monitoring of the temperature field within HIFU focal area remains a challenge. Although ultrasound technology, compared with other approaches, is a good choice for noninvasive and real-time monitoring on the temperature distribution, traditional ultrasonic thermometry mainly relies on the backscattered signal, which is difficult for high temperature (>50 °C) measurement. Given that artificial intelligence (AI) shows significant potential for biomedical applications, we propose an AI-powered ultrasonic thermometry using an end-to-end deep neural network termed Breath-guided Multimodal Teacher-Student (BMTS), which possesses the capability to elucidate the interaction between HIFU and complex heterogeneous biological media. It has been demonstrated experimentally that two-dimension temperature distribution within HIFU focal area in deep organ can be accurately reconstructed with an average error and a frame speed of 0.8 °C and 0.37 s, respectively. Most importantly, the maximum measurable temperature for ultrasonic technology has been successfully expanded to a record value of 67 °C. This breakthrough indicates that the development of AI-powered ultrasonic thermometry is beneficial for precise HIFU therapy planning in the future.

中文翻译：

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人工智能驱动的超声测温法用于深部器官 HIFU 治疗


高强度聚焦超声 （HIFU） 被认为是深部器官肿瘤消融的重要非侵入性方法。然而，准确实时监测 HIFU 病灶区域内的温度场仍然是一个挑战。虽然超声技术与其他方法相比，是无创、实时监测温度分布的较佳选择，但传统的超声测温法主要依赖于背向散射信号，难以进行高温（>50 °C）测量。鉴于人工智能 （AI） 在生物医学应用方面显示出巨大的潜力，我们提出了一种人工智能驱动的超声测温法，使用称为呼吸引导多模态师生 （BMTS） 的端到端深度神经网络，它能够阐明 HIFU 与复杂的异质生物介质之间的相互作用。实验证明，深部器官 HIFU 病灶区域内的二维温度分布可以准确重建，平均误差和 0.8 °C 和 0.37 s 的帧速分别为 0.8 °C 和 0.37 s。最重要的是，超声波技术的最高可测量温度已成功扩展到创纪录的 67 °C。 这一突破表明，人工智能超声测温技术的发展有利于未来精确的 HIFU 治疗规划。