Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles
Real-Time Radiofrequency Ablation Lesion Depth Estimation Using Multi-frequency Impedance With a Deep Neural Network and Tree-Based Ensembles
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DOI：
10.1109/tbme.2019.2950342
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发表时间：
2020-07
影响因子：
4.6
通讯作者：
E. Besler;Yearnchee Curtis Wang;A. Sahakian
中科院分区：
文献类型：
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作者：
E. Besler;Yearnchee Curtis Wang;A. Sahakian
目的：设计和优化用于软实时性能射频消融（RFA）损伤深度估计方法的统计模型。方法：利用低成本嵌入式系统采集的组织多频复杂电阻抗数据，训练深度神经网络（NN）和基于树的集合（te），通过回归估计RFA病变深度。结果：增加频率扫描数据、以前的深度数据和以前的射频功率状态数据提高了统计模型的准确性。对于以往的统计模型，神经网络的均方根误差为2mm， TEs的均方根误差为0.5 mm；对于本文提出的统计模型，神经网络的均方根误差为0.4 mm， TEs的均方根误差为0.04 mm。模拟烧蚀性能与物理测量值的平均差异显示，基于神经网络的深度估计方法为0.5 \pm 0.2$ mm，基于te的深度估计方法为0.7 \pm 0.4$ mm。结论：多频数据显著提高了统计模型的深度估计性能。意义：本研究提出的RFA病变深度估计方法在基于armv7的嵌入式系统上实现了毫米级分辨率的精度，具有软实时性能，有可能转化为临床RFA技术。
Objective: Design and optimization of statistical models for use in methods for estimating radiofrequency ablation (RFA) lesion depths in soft real-time performance. Methods: Using tissue multi-frequency complex electrical impedance data collected from a low-cost embedded system, a deep neural network (NN) and tree-based ensembles (TEs) were trained for estimating the RFA lesion depth via regression. Results: Addition of frequency sweep data, previous depth data, and previous RF power state data boosted accuracy of the statistical models. The root mean square errors were 2 mm for NN and 0.5 mm for TEs for previous statistical models and the root mean square errors were 0.4 mm for NN and 0.04 mm for TEs for the statistical models presented in this paper. Simulation ablation performance showed a mean difference against physical measurements of $0.5 \pm 0.2$ mm for the NN-based depth estimation method and $0.7 \pm 0.4$ mm for the TE-based depth estimation method. Conclusion: The results show that multi-frequency data significantly improves the depth estimation performance of the statistical models. Significance: The RFA lesion depth estimation methods presented in this work achieve millimeter-resolution accuracy with soft real-time performance on an ARMv7-based embedded system for potential translation to clinical RFA technologies.