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Leveraging spatial uncertainty for online error compensation in EMT.
Henry Krumb1, Sofie Hofmann2, David Kügler3
1Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany. henry.john.krumb@gris.tu-darmstadt.de.
Summary
Artificial neural networks (ANNs) effectively compensate for electromagnetic tracking (EMT) errors, outperforming traditional methods. This advancement reduces the need for X-ray imaging in hybrid navigation, improving patient safety.
Area of Science:
- Medical imaging and navigation
- Artificial intelligence in healthcare
- Biomedical engineering
Background:
- Electromagnetic tracking (EMT) offers potential for reduced radiation exposure in hybrid procedures.
- EMT systems are prone to distortions, necessitating algorithmic error compensation.
- Current compensation algorithms for EMT in guidewire procedures are limited to online applications.
Purpose of the Study:
- To develop and evaluate an artificial neural network (ANN) for compensating EMT navigation errors.
- To assess the performance of ANNs in both online and offline scenarios compared to polynomial fits.
- To investigate the utility of spatial uncertainty assessment in ANN-based EMT error compensation.
Main Methods:
- Trained a symmetric artificial neural network (ANN) architecture using collected positional data.
- Evaluated ANN compensation performance against polynomial regression in online and offline settings.
- Assessed spatial uncertainty of ANN compensation and its impact on accuracy and radiation exposure via simulations.
Main Results:
- ANNs achieved over 70% compensation for unseen distortions, surpassing polynomial regression.
- ANNs demonstrated superior performance on known distortions compared to polynomial methods.
- A linear correlation between tracking accuracy and model uncertainty was empirically established.
Conclusions:
- ANNs are effective for EMT error compensation and generalize to new distortions.
- Assessing model uncertainty is crucial for optimizing training data and improving spatial error compensation algorithms.
- EMT error compensation significantly reduces the requirement for X-ray imaging in hybrid navigation.
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