非均匀介质中非侵入性温度估计的NARX结构

C. Teixeira, W. Pereira, A. Ruano, M. Ruano
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引用次数: 6

摘要

热疗法的安全有效应用受到精确的非侵入性温度估计器存在的限制。这样的估计器可以通过正确的仪器控制在感兴趣的区域上实现正确的功率沉积。在多层介质中,应估计每层的温度,特别是在治疗过程中发生显著温度变化的界面。在这项工作中,一个带有外源输入的非线性自回归结构(NARX)被应用于多层(非均匀)介质中的无创估计温度,同时提交给物理治疗超声。NARX结构由静态前馈径向基函数神经网络(RBFNN)组成,其输入引起外部动态。采用多目标遗传算法对NARX结构参数进行优化。在四种辐射强度下,获得的最佳模型在界面和内层点上的最大绝对误差均低于0.5℃(热疗法/透热疗法的建议阈值)。这些模型在实时应用中也具有较小的计算复杂度。据我们所知,这是第一个在多层介质中使用超声加热和估计的非侵入性估计方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NARX structures for non-invasive temperature estimation in non-homogeneous media
The safe and effective application of thermal therapies are limited by the existence of precise non-invasive temperature estimators. Such estimators would enable a correct power deposition on the region of interest by means of a correct instrumentation control. In multi-layered media, the temperature should be estimated at each layer and especially at the interfaces, where significant temperature changes should occur during therapy. In this work, a non-linear autoregressive structure with exogenous inputs (NARX) was applied to non-invasively estimate temperature in a multi-layered (non-homogeneous) medium, while submitted to physiotherapeutic ultrasound. The NARX structure is composed by a static feed-forward radial basis functions neural network (RBFNN), with external dynamics induced by its inputs. The NARX structure parameters were optimized by means of a multi-objective genetic algorithm. The best attained models reached a maximum absolute error inferior to 0.5degC (proposed threshold in hyperthermia/diathermia) at both the interface and inner layer points, at four radiation intensities. These models present also a small computational complexity as desired for real-time applications. To the best of ours knowledge this is the first non-invasive estimation approach in multi-layered media using ultrasound for both heating and estimation.
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