HEX: a safe research framework for hybrid EMT X-ray navigation.

PURPOSE: Navigating with continuous X-ray provides visual guidance, but exposes both surgeon and patient to ionizing radiation, which is associated with serious health risks. Interleaving fluoro snapshots with electromagnetic tracking (EMT) potentially minimizes radiation. METHODS: We propose hybrid EMT + X-ray (HEX), a research framework for navigation with an emphasis on safe experimentation. HEX is based on several hardware and software components that are orchestrated to allow for safe and efficient data acquisition. RESULTS: In our study, hybrid navigation reduces radiation by [Formula: see text] with cubic, and by [Formula: see text] with linear error compensation while achieving submillimeter accuracy. Training points for compensation can be reduced by half while keeping a similar accuracy-radiation trade-off. CONCLUSION: The HEX framework allows to safely and efficiently evaluate the hybrid navigation approach in simulated procedures. Complementing intraoperative X-ray with EMT significantly reduces radiation in the OR, increasing the safety of patients and surgeons.

CycleGAN for interpretable online EMT compensation.

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.

Leveraging spatial uncertainty for online error compensation in EMT.

PURPOSE: Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmically. Compensation algorithms for EMT in guidewire procedures are only practical in an online setting. METHODS: We collect positional data and train a symmetric artificial neural network (ANN) architecture for compensating navigation error. The results are evaluated in both online and offline scenarios and are compared to polynomial fits. We assess spatial uncertainty of the compensation proposed by the ANN. Simulations based on real data show how this uncertainty measure can be utilized to improve accuracy and limit radiation exposure in hybrid navigation. RESULTS: ANNs compensate unseen distortions by more than 70%, outperforming polynomial regression. Working on known distortions, ANNs outperform polynomials as well. We empirically demonstrate a linear relationship between tracking accuracy and model uncertainty. The effectiveness of hybrid tracking is shown in a simulation experiment. CONCLUSION: ANNs are suitable for EMT error compensation and can generalize across unseen distortions. Model uncertainty needs to be assessed when spatial error compensation algorithms are developed, so that training data collection can be optimized. Finally, we find that error compensation in EMT reduces the need for X-ray images in hybrid navigation.

High-precision evaluation of electromagnetic tracking.

PURPOSE: Navigation in high-precision minimally invasive surgery (HP-MIS) demands high tracking accuracy in the absence of line of sight (LOS). Currently, no tracking technology can satisfy this requirement. Electromagnetic tracking (EMT) is the best tracking paradigm in the absence of LOS despite limited accuracy and robustness. Novel evaluation protocols are needed to ensure high-precision and robust EMT for navigation in HP-MIS. METHODS: We introduce a novel protocol for EMT measurement evaluation featuring a high-accuracy phantom based on LEGO[Formula: see text], which is calibrated by a coordinate measuring machine to ensure accuracy. Our protocol includes relative sequential positions and an uncertainty estimation of positioning. We show effects on distortion compensation using a learned interpolation model. RESULTS: Our high-precision protocol clarifies properties of errors and uncertainties of EMT for high-precision use cases. For EMT errors reaching clinically relevant 0.2 mm, our design is 5-10 times more accurate than previous protocols with 95% confidence margins of 0.02 mm. This high-precision protocol ensures the performance improvement in compensated EMT by 0.05 mm. CONCLUSION: Our protocol improves the reliability of EMT evaluations because of significantly lower protocol-inherent uncertainties. To reduce patient risk in HP-MIS and to evaluate magnetic field distortion compensation, more high-accuracy protocols such as the one proposed here are required.