Krumb, Henry ; Das, Dhritimaan ; Chadda, Romol ; Mukhopadhyay, Anirban (2024)
CycleGAN for interpretable online EMT compensation.
In: International Journal of Computer Assisted Radiology and Surgery, 2021, 16 (5)
doi: 10.26083/tuprints-00023529
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
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.
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.
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.
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: | 2024 |
| Autor(en): | Krumb, Henry ; Das, Dhritimaan ; Chadda, Romol ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | CycleGAN for interpretable online EMT compensation |
| Sprache: | Englisch |
| Publikationsjahr: | 24 September 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Ort der Erstveröffentlichung: | Berlin ; Heidelberg |
| Verlag: | Springer International Publishing |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Computer Assisted Radiology and Surgery |
| Jahrgang/Volume einer Zeitschrift: | 16 |
| (Heft-)Nummer: | 5 |
| DOI: | 10.26083/tuprints-00023529 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23529 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | 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. 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. 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. 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, Hybrid navigation, Generative adversarial networks, Adversarial domain adaptation |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-235296 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Mess- und Sensortechnik 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 24 Sep 2024 09:10 |
| Letzte Änderung: | 30 Sep 2024 11:29 |
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- CycleGAN for interpretable online EMT compensation. (deposited 24 Sep 2024 09:10) [Gegenwärtig angezeigt]
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