a Faculty of Electronic Engineering (FEE) , Menoufia University , Menouf , Egypt.
Network. 2018;29(1-4):20-36. doi: 10.1080/0954898X.2018.1539260. Epub 2018 Nov 8.
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.
热剂量是高温热疗癌症治疗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，用于高强度聚焦超声（HIFU）短时间脉冲治疗过程中的癌症肿瘤高温热疗热剂量。所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，与之前研究中的其他控制器相比，完全调整的 RBF 神经网络控制器通过实现最短治疗时间小于 13.5 分钟的目标热剂量，避免了热疗期间的组织空化，从而取得了更好的性能。此外，其平均积分时间绝对误差（MTAE）的最大值为 98.64，显著小于在相同治疗条件下所有测试案例中手动调整控制器的误差。在这项研究中，集成了 ACO 方法的鲁棒 RBF 神经网络控制器为使用聚焦超声换能器进行精确的高温热疗癌症肿瘤治疗提供了成功且改进的性能，而无需外部冷却效果。
