PDF(Pubmed)
Abstract：
Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25-65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues.
摘要：
微波热疗(MH)期间的实时准确温度监测仍然是确保治疗效果和患者安全的关键挑战。这项研究提出了一种新颖的方法来模拟真实的MH，并使用时间信息神经网络精确确定生物组织内目标区域的温度。我们对30套体模和10套离体猪肉组织进行了MH实验。我们提出了一个新的观点：对连续电磁辐射刺激的组织反应的演变是时间和空间维度的联合演变。我们的模型利用TimesNet提取周期性特征，利用Cloblock从超声图像中捕获二维周期性矢量中的全局信息相关性。通过吸收更多的超声时间数据，我们的模型提高了温度估计的准确性。在25-65°C的温度范围内，我们的神经网络对新鲜离体猪肉组织和体模实现了约0.886°C和0.419°C的温度估计均方根误差，分别。所提出的时间通知神经网络具有适度的参数计数，渲染它适合部署在超声波移动设备。此外，它达到了接近临床标准规定的温度精度，使其在生物组织MH期间的无损温度监测有效。
