Krumb, Henry ; Das, Dhritimaan ; Chadda, Romol ; Mukhopadhyay, Anirban (2021)
CycleGAN for interpretable online EMT compensation.
In: International Journal of Computer Assisted Radiology and Surgery
doi: 10.1007/s11548-021-02324-1
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Krumb, Henry ; Das, Dhritimaan ; Chadda, Romol ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | CycleGAN for interpretable online EMT compensation |
| Sprache: | Englisch |
| Publikationsjahr: | 14 März 2021 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Computer Assisted Radiology and Surgery |
| DOI: | 10.1007/s11548-021-02324-1 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Electromagnetic tracking |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 25 Mär 2021 11:01 |
| Letzte Änderung: | 25 Mär 2021 11:01 |
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