Purpose

Electromagnetic tracking (EMT) can partially replace X-ray guidance in minimally invasive procedures, reducing radiation in the OR. However, in this hybrid setting, EMT is disturbed by metallic distortion caused by the X-ray device. We plan to make hybrid navigation clinical reality to reduce radiation exposure for patients and surgeons, by compensating EMT error.

Methods

Our online compensation strategy exploits cycle-consistent generative adversarial neural networks (CycleGAN). Positions are translated from various bedside environments to their bench equivalents, by adjusting their z-component. Domain-translated points are fine-tuned on the x–y plane to reduce error in the bench domain. We evaluate our compensation approach in a phantom experiment.

Results

Since the domain-translation approach maps distorted points to their laboratory equivalents, predictions are consistent among different C-arm environments. Error is successfully reduced in all evaluation environments. Our qualitative phantom experiment demonstrates that our approach generalizes well to an unseen C-arm environment.

Conclusion

Adversarial, cycle-consistent training is an explicable, consistent and thus interpretable approach for online error compensation. Qualitative assessment of EMT error compensation gives a glimpse to the potential of our method for rotational error compensation.

中文翻译：

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CycleGAN 用于可解释的在线 EMT 补偿

 目的

电磁跟踪 (EMT) 可以部分取代微创手术中的 X 射线引导，减少手术室中的辐射。然而，在这种混合环境中，EMT 受到 X 射线设备引起的金属变形的干扰。我们计划通过补偿 EMT 误差，使混合导航临床成为现实，以减少患者和外科医生的辐射暴露。

 方法

我们的在线补偿策略利用循环一致的生成对抗神经网络（CycleGAN）。通过调整 z 分量，可以将位置从各种床边环境转换为相应的工作台环境。域平移点在 x-y 平面上进行微调，以减少工作台域中的误差。我们在虚拟实验中评估了我们的补偿方法。

 结果

由于域转换方法将扭曲点映射到其实验室等效点，因此预测在不同的 C 臂环境中是一致的。在所有评估环境中都成功减少了错误。我们的定性模型实验表明，我们的方法可以很好地推广到不可见的 C 形臂环境。

 结论

对抗性、周期一致的训练是一种可解释、一致且可解释的在线误差补偿方法。 EMT 误差补偿的定性评估让我们一睹我们的旋转误差补偿方法的潜力。