Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch

Karstensen L, Ritter J, Hatzl J, Ernst F, Langejürgen J, Uhl C, Mathis-Ullrich F (2023)

Publication Language: English

Publication Type: Journal article

Publication year: 2023

Journal

International Journal of Computer Assisted Radiology and Surgery Springer Verlag (Germany)

Abstract

Purpose: Endovascular intervention is the state-of-the-art treatment for common cardiovascular diseases, such as heart attack and stroke. Automation of the procedure may improve the working conditions of physicians and provide high-quality care to patients in remote areas, posing a major impact on overall treatment quality. However, this requires the adaption to individual patient anatomies, which currently poses an unsolved challenge. Methods: This work investigates an endovascular guidewire controller architecture based on recurrent neural networks. The controller is evaluated in-silico on its ability to adapt to new vessel geometries when navigating through the aortic arch. The controller’s generalization capabilities are examined by reducing the number of variations seen during training. For this purpose, an endovascular simulation environment is introduced, which allows guidewire navigation in a parametrizable aortic arch. Results: The recurrent controller achieves a higher navigation success rate of 75.0% after 29,200 interventions compared to 71.6% after 156,800 interventions for a feedforward controller. Furthermore, the recurrent controller generalizes to previously unseen aortic arches and is robust towards size changes of the aortic arch. Being trained on 2048 aortic arch geometries gives the same results as being trained with full variation when evaluated on 1000 different geometries. For interpolation a gap of 30% of the scaling range and for extrapolation additional 10% of the scaling range can be navigated successfully. Conclusion: Adaption to new vessel geometries is essential in the navigation of endovascular instruments. Therefore, the intrinsic generalization to new vessel geometries poses an essential step towards autonomous endovascular robotics.

Authors with CRIS profile

Lennart Karstensen Professur für Robotische Planung und Kognition in der Chirurgie Franziska Mathis-Ullrich Professur für Robotische Planung und Kognition in der Chirurgie

Involved external institutions

Universität zu Lübeck

Germany (DE) Universitätsklinikum Heidelberg

Germany (DE)

How to cite

APA:

Karstensen, L., Ritter, J., Hatzl, J., Ernst, F., Langejürgen, J., Uhl, C., & Mathis-Ullrich, F. (2023). Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch. International Journal of Computer Assisted Radiology and Surgery. https://dx.doi.org/10.1007/s11548-023-02938-7

MLA:

Karstensen, Lennart, et al. "Recurrent neural networks for generalization towards the vessel geometry in autonomous endovascular guidewire navigation in the aortic arch." International Journal of Computer Assisted Radiology and Surgery (2023).

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