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SENSITIVITY ANALYSIS FOR THE OPTIMIZATION OF RADIOFREQUENCY ABLATION IN THE PRESENCE OF MATERIAL PARAMETER UNCERTAINTY

机译:存在材料参数不确定性时的射频消融优化灵敏度分析

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We present a sensitivity analysis of the optimization of the probe placement in radiofrequency (RF) ablation which takes the uncertainty associated with biophysical tissue properties (electrical and thermal conductivity) into account. Our forward simulation of RF ablation is based upon a system of partial differential equations (PDEs) that describe the electric potential of the probe and the steady state of the induced heat. The probe placement is optimized by minimizing a temperature-based objective function such that the volume of destroyed tumor tissue is maximized. The resulting optimality system is solved with a multilevel gradient descent approach. By evaluating the corresponding optimality system for certain realizations of tissue parameters (i.e., at certain, well-chosen points in the stochastic space) the sensitivity of the system can be analyzed with respect to variations in the tissue parameters. For the interpolation in the stochastic space we use an adaptive sparse grid collocation (ASGC) approach presented by Ma and Zabaras. We underscore the significance of the approach by applying the optimization to CT data obtained from a real KF ablation case.
机译:我们提出了在射频(RF)消融中优化探头放置的敏感性分析,其中考虑了与生物物理组织特性(电导率和热导率)相关的不确定性。我们对射频消融的正向仿真基于偏微分方程(PDE)系统,该系统描述了探针的电势和感应热的稳态。通过最小化基于温度的目标函数来优化探针放置,以使被破坏的肿瘤组织的体积最大化。最终的最优系统通过多级梯度下降法求解。通过针对组织参数的某些实现(即,在随机空间中的某些适当选择的点)评估相应的最优系统,可以针对组织参数的变化来分析系统的灵敏度。对于随机空间中的插值,我们使用了Ma和Zabaras提出的自适应稀疏网格配置(ASGC)方法。通过将优化应用于从实际KF消融病例获得的CT数据,我们强调了该方法的重要性。

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