  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). 3D positions are translated
from various bedside environments to their bench equivalents. Domain-translated
points are fine-tuned 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
lab 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.
