TU Darmstadt / ULB / TUbiblio

Leveraging spatial uncertainty for online error compensation in EMT

Krumb, Henry and Hofmann, Sofie and Kügler, David and Ghazy, Ahmed and Dorweiler, Bernhard and Bredemann, Judith and Schmitt, Robert and Sakas, Georgios and Mukhopadhyay, Anirban (2020):
Leveraging spatial uncertainty for online error compensation in EMT.
In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410,
DOI: 10.1007/s11548-020-02189-w,
[Article]

Abstract

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.

Item Type: Article
Erschienen: 2020
Creators: Krumb, Henry and Hofmann, Sofie and Kügler, David and Ghazy, Ahmed and Dorweiler, Bernhard and Bredemann, Judith and Schmitt, Robert and Sakas, Georgios and Mukhopadhyay, Anirban
Title: Leveraging spatial uncertainty for online error compensation in EMT
Language: English
Abstract:

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.

Journal or Publication Title: International Journal of Computer Assisted Radiology and Surgery
Uncontrolled Keywords: Electromagnetic tracking
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 02 Jun 2020 10:06
DOI: 10.1007/s11548-020-02189-w
Official URL: https://doi.org/10.1007/s11548-020-02189-w
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details