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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 5/2021

Open Access 01-05-2021 | Original Article

CycleGAN for interpretable online EMT compensation

Authors: Henry Krumb, Dhritimaan Das, Romol Chadda, Anirban Mukhopadhyay

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2021

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Abstract

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.
Appendix
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Metadata
Title
CycleGAN for interpretable online EMT compensation
Authors
Henry Krumb
Dhritimaan Das
Romol Chadda
Anirban Mukhopadhyay
Publication date
01-05-2021
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 5/2021
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-021-02324-1

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