ABSTRACT Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) neural network controller for high-temperature hyperthermia thermal dose during the therapeutic procedure of cancer tumours by short-time pulses of high-intensity focused ultrasound (HIFU). The developed controller is stabilized and automatically tuned based on Lyapunov functions and ant colony optimization (ACO) algorithm, respectively. In addition, this thermal dose control system has been validated using one-dimensional (1-D) biothermal tissue model. Simulation results showed that the fully tuned RBF neural network controller outperforms other controllers in the previous studies by achieving targeted thermal dose with shortest treatment times less than 13.5 min, avoiding the tissue cavitation during the thermal therapy. Moreover, the maximum value of its mean integral time absolute error (MTAE) is 98.64, which is significantly less than the resulted errors for the manual-tuned controller under the same treatment conditions of all tested cases. In this study, integrated ACO method with robust RBF neural network controller provides a successful and improved performance to deliver accurate thermal dose of hyperthermia cancer tumour treatment using the focused ultrasound transducer without external cooling effect.

中文翻译：

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用于超声热疗癌症肿瘤治疗的全调谐 RBF 神经网络控制器

摘要 热剂量是热疗治疗癌症的重要临床疗效指标。本文提出了一种新的直接径向基函数 (RBF) 神经网络控制器，用于在癌症肿瘤治疗过程中通过高强度聚焦超声 (HIFU) 的短时脉冲进行高温热疗。开发的控制器分别基于李雅普诺夫函数和蚁群优化 (ACO) 算法进行稳定和自动调整。此外，该热剂量控制系统已使用一维 (1-D) 生物热组织模型进行了验证。仿真结果表明，完全调谐的 RBF 神经网络控制器通过以小于 13.5 分钟的最短治疗时间实现目标热剂量，优于以往研究中的其他控制器，避免热疗过程中组织空化。此外，其平均积分时间绝对误差（MTAE）的最大值为 98.64，显着小于所有测试案例在相同处理条件下手动调整控制器的结果误差。在这项研究中，集成 ACO 方法与强大的 RBF 神经网络控制器提供了成功和改进的性能，使用聚焦超声换能器提供准确的热剂量热疗癌症肿瘤治疗，无需外部冷却效果。