Aabha Tamhankar

Aabha Tamhankar

PhD Candidate, Robotics Engineering

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Towards Autonomous Navigation of Neuroendovascular Tools for Timely Stroke Treatment via Contact-Aware Path Planning

Published in ISMR, 2025

In this paper, we propose a model-based contactaware motion planner for autonomous navigation of neuroendovascular tools in acute ischemic stroke. The planner is designed to find the optimal control strategy for telescopic pre-bent catheterization tools such as guidewire and catheters, currently used for neuroendovascular procedures. A kinematic model for the telescoping tools and their interaction with the surrounding anatomy is derived to predict tools steering. By leveraging geometrical knowledge of the anatomy, obtained from pre-operative segmented 3D images, and the mechanics of the telescoping tools, the planner finds paths to the target enabled by interacting with the surroundings. We propose an actuation platform for insertion and rotation of the telescopic tools and present experimental results for the navigation from the base of the descending aorta to the Left Common Carotid Artery (LCCA). We demonstrate that, by leveraging the preoperative plan, we can consistently navigate the LCCA with 100% success of over 50 independent trials. We also study the robustness of the planner towards motion of the aorta and errors in the initial positioning of the robotic tools. The proposed plan can successfully reach the LCCA for rotations of the aorta of up to 10°, and displacement of up to 10 mm, on the coronal plane.

Recommended citation: A. Tamhankar and G. Pittiglio, "Towards Autonomous Navigation of Neuroendovascular Tools for Timely Stroke Treatment via Contact-Aware Path Planning," 2025 International Symposium on Medical Robotics (ISMR), Atlanta, GA, USA, 2025, pp. 23-29, doi: 10.1109/ISMR67322.2025.11025974.
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Contact-aware Path Planning for Autonomous Neuroendovascular Navigation

Published in R-AL, 2026

We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated in diverse and representative anatomies of the vessels. In these anatomical demonstrators, the algorithm shows a 100% convergence rate within 22.8s in the worst case, with sub-millimeter tracking errors (less than 0.64 mm), and is found effective on anatomical phantoms representative of around 94% of patients.s

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