Abstract
PurposeElectromagnetic 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.MethodsWe 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.ResultsANNs 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.ConclusionANNs 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.
Highlights
Electromagnetic tracking (EMT) is a key technology to enable navigation in minimally invasive surgery without line of sight
As miniaturized sensors can be integrated into catheters, EMT has potential to be employed for guidewire navigation in abdominal aortic aneurysm repair (AAAR) [15,16]
We present a novel active error compensation framework for EMT in endovascular surgery
Summary
Electromagnetic tracking (EMT) is a key technology to enable navigation in minimally invasive surgery without line of sight. Fluoroscopic X-ray imaging is considered the gold standard for guidewire navigation in endovascular aneurysm repair [5]. A more realistic approach is to consider a hybrid navigation framework. In this framework, continuous navigation will be performed by radiation-free EMT, while X-ray snapshots will be acquired on demand for recalibration or dexterous maneuver. Continuous navigation will be performed by radiation-free EMT, while X-ray snapshots will be acquired on demand for recalibration or dexterous maneuver This hybrid navigation reduces the amount of Xray images that need to be captured during the procedure, which in turn will reduce the radiation exposure for both surgeon and patient
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