CycleGAN for Interpretable Online EMT Compensation
This work aims to reduce radiation exposure for patients and surgeons in minimally invasive procedures by improving the accuracy of electromagnetic tracking in hybrid operating room settings. It represents an incremental improvement in EMT compensation.
This paper addresses the problem of electromagnetic tracking (EMT) errors caused by metallic distortion from X-ray devices in hybrid surgical settings. The authors propose an online compensation strategy using CycleGAN to translate distorted 3D positions to their bench equivalents, successfully reducing error across various C-arm environments.
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.