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Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

机译:基于密度分布和微创机器人人工耳蜗植入体内实验的骨钻温度预测模型

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摘要

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.
机译:已经提出离体外科手术机器人以在颞骨中钻出精确的孔,以进行微创的人工耳蜗植入。该过程的主要风险是由于机械相互作用或由于在钻孔过程中温度升高而损坏面神经。为了评估钻孔过程的热风险,提出了一种简化的模型,该模型旨在针对不同乳突骨密度的一组给定的恒定过程参数,评估对面神经造成的风险。该模型使用沿着乳突状骨中的钻探轨迹的骨密度分布来计算钻头尖端处随时间变化的热量产生函数。使用随时间变化的移动点源格林函数,可以在空间中的某个点求解热方程,以便可以随时间计算最终温度。对该模型进行校准,并首先使用体内温度数据进行验证。数据是通过微创机器人在四只不同的绵羊中钻12个孔而收集的。麻醉绵羊,并用热电偶测量温度升高,该热电偶插入先前计划的钻孔轨迹旁边的先前钻孔中。通过在整个钻头直径上平均Hounsfield值,从术前CT数据中提取骨密度分布。术后[公式:参见文本] CT数据用于验证轨迹的钻孔精度。测量温度和计算温度的比较表明,加热和冷却阶段都非常匹配。最高温度的平均预测误差小于0.7°C,平均均方根误差约为0.5°C。为了分析潜在的热损伤,该模型用于计算温度曲线和43°C下距面神经最短距离的累积等效分钟数。对于选定的钻孔参数,温度升高曲线和累积等效分钟数表明,这种微创人工耳蜗植入手术的热升高可能会对面神经造成危险,尤其是在硬化性或高密度乳突骨中。需要评估优化的钻井参数,并且该模型可以用于将来的风险评估。

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