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Autonomous guidewire navigation in a two dimensional vascular phantom

2020; De Gruyter; Volume: 6; Issue: 1 Linguagem: Inglês

10.1515/cdbme-2020-0007

2364-5504

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

Teleoperation and Haptic Systems

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.