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Abstract #4839

Physics-Driven Passive Marker Tracking for MR-Guided Endovascular Interventions

Anieck ter Braak1, Evangelia Ilia1, Martin Reinok2, Giulio Dagnino2, and Wyger Brink1
1Magnetic Detection and Imaging group, TechMed Centre, University of Twente, Enschede, Netherlands, 2Robotics and Mechatronics group, Faculty of EEMCS, University of Twente, Enschede, Netherlands

Synopsis

Keywords: Interventional Devices, Interventional Devices, Passive tracking

Motivation: Endovascular interventions are compromised by the lack of interactive slice tracking during procedures.

Goal(s): Using a physic-based marker model to establish an automated passive tracking method.

Approach: Synthetic training data was generated and used to train a neural network for the detection of susceptibility markers. The dynamic tracking performance was evaluated in a vascular phantom.

Results: The model can accurately simulate various passive marker appearances across arbitrary orientations and slice alignments, and facilitate AI-based passive tracking during MR-guided catheter interventions.

Impact: Our physics-based approach allows for generating large annotated training datasets for passive marker detection, enabling robust tracking applications based on AI. This approach can improve the robustness during automated tracking of guidewires in MR-guided endovascular interventions.

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Keywords