基于表面热成像传感的三维电外科手术过程内域温度估计的无限维自适应边界观测器
Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing
摘要：
我们提出了一种新的3D自适应观察员框架，用于确定电外科手术中的表面下有机组织温度。观测器结构利用从实时红外热像仪获得的逐点2D表面温度读数进行参数估计和温度场观测。我们介绍了一种新的方法来解耦参数自适应和估计，其中参数估计可以实时运行，而观察员循环运行在一个较慢的时间尺度。为了实现这一目标，我们引入了一种新的参数估计方法，称为基于注意力的噪声鲁棒平均，其中表面热成像时间序列被用来直接估计组织的扩散率。我们的观察员包含一个实时的参数自适应组件的基础上，这个扩散率适应法，以及一个Luenberger型校正器的基础上感测的表面温度。在这项工作中，我们还提出了一种新的模型结构，适用于机器人手术的设置，其中我们模型的电外科热分布作为一个复杂的支持幅度和速度控制的热源，涉及一个新的非线性输入映射。我们证明了令人满意的性能的自适应观察员在模拟中，使用现实生活中的实验离体猪组织数据。
英文摘要：
We present a novel 3D adaptive observer framework for use in the determination of subsurface organic tissue temperatures in electrosurgery. The observer structure leverages pointwise 2D surface temperature readings obtained from a real-time infrared thermographer for both parameter estimation and temperature field observation. We introduce a novel approach to decoupled parameter adaptation and estimation, wherein the parameter estimation can run in real-time, while the observer loop runs on a slower time scale. To achieve this, we introduce a novel parameter estimation method known as attention-based noise-robust averaging, in which surface thermography time series are used to directly estimate the tissue's diffusivity. Our observer contains a real-time parameter adaptation component based on this diffusivity adaptation law, as well as a Luenberger-type corrector based on the sensed surface temperature. In this work, we also present a novel model structure adapted to the setting of robotic surgery, wherein we model the electrosurgical heat distribution as a compactly supported magnitude- and velocity-controlled heat source involving a new nonlinear input mapping. We demonstrate satisfactory performance of the adaptive observer in simulation, using real-life experimental ex vivo porcine tissue data.
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