Deep-learning-based Position Control of a Robotic Catheter under Environmental Contact

Di Wu*, Yao Zhang, Mouloud Ourak, Xuan Thao Ha, Kenan Niu, Jenny Dankelman, Emmanuel Vander Poorten

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Precise control of robotic catheters remains challenging in interventions. Inherent non-linearities such as hysteresis and external disturbances such as blood flow or contact with the vessel walls have a large impact on the reachable positioning precision. As inaccurate positioning of the catheter tip could lead to tissue damage, controllers that would perform adequately in the presence of hysteresis and environmental contacts would be highly desirable. This paper proposes a method based on multiple Long Short-Term Memory Networks (LSTMs). To this end, a so-called free-space-LSTM (f-LSTM) is trained in order to steer the catheter when it moves in free. Constrained-space-LSTMs (c-LSTMs) are trained to drive the catheter when it is in contact with an obstacle. Based on contact estimation methods, LSTMs are switched. The f-LSTM and c-LSTMs are first tested in free space motion and under constraint situations. The results reveal that LSTMs perform well (RMSE < 0.5 mm) for a steerable robot section with a total length of 77 mm when tested in the same situation where trained. However, when f-LSTM and c-LSTM were tested in an environment different from the one in which they were trained, errors tended to increase. The results highlight the need to exhaustively estimate the contact location and switch between different LSTMs accordingly. The effective working range of a c-LSTM was investigated as well. Experiments showed that a well-Trained single c-LSTM could be used effectively in a range of 8.8 mm among the entire length of a steerable catheter section, while maintaining the average tip positioning error below 2 mm in this range.

Original languageEnglish
Title of host publicationProceedings of the International Symposium on Medical Robotics, ISMR 2022
PublisherIEEE
Number of pages7
ISBN (Electronic)9781665469289
DOIs
Publication statusPublished - 2022
Event2022 International Symposium on Medical Robotics, ISMR 2022 - Atlanta, United States
Duration: 13 Apr 202215 Apr 2022

Conference

Conference2022 International Symposium on Medical Robotics, ISMR 2022
Country/TerritoryUnited States
CityAtlanta
Period13/04/2215/04/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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