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Autonomous guidewire navigation in a two dimensional vascular phantom

Karstensen, Lennart; Behr, Tobias; Pusch, Tim Philipp; Mathis-Ullrich, Franziska; Stallkamp, Jan

Abstract:
The treatment of cerebro- and cardiovascular diseases requires complex and challenging navigation of a catheter. Previous attempts to automate catheter navigation lack the ability to be generalizable. Methods of Deep Reinforcement Learning show promising results and may be the key to automate catheter navigation through the tortuous vascular tree. This work investigates Deep Reinforcement Learning for guidewire manipulation in a complex and rigid vascular model in 2D. The neural network trained by Deep Deterministic Policy Gradients with Hindsight Experience Replay performs well on the low-level control task, however the high-level control of the path planning must be improved further.


Verlagsausgabe §
DOI: 10.5445/IR/1000123824
Veröffentlicht am 29.10.2021
Originalveröffentlichung
DOI: 10.1515/cdbme-2020-0007
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 2364-5504
KITopen-ID: 1000123824
Erschienen in Current directions in biomedical engineering
Verlag De Gruyter
Band 6
Heft 1
Seiten Art.Nr. 20200007
Vorab online veröffentlicht am 17.09.2020
Schlagwörter autonomous; catheter navigation; deep reinforcement learning; machine learning; neural network controller
Nachgewiesen in Dimensions
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