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Abstract #2048

Prospective AI-based Slice Tracking for Continuous MRI Visualization of Gadolinium-Filled Cardiac Catheters

Alexander Paul Neofytou1, Grzegorz Tomasz Kowalik1, Rohini Vidya Shankar1, Karl Kunze1,2, Tracy Moon3, Nina Mellor3, Radhouene Neji1, Reza Razavi1, Kuberan Pushparajah1,3, and Sébastien Roujol 1
1Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, 3Department of Paediatric Cardiology, Evelina London Children's Hospital, London, United Kingdom

Synopsis

Keywords: MR-Guided Interventions, MR-Guided Interventions, cardiac catheterization, passive tracking, real time, deep learning

Motivation: MRI-guided cardiac catheterization relies on manual slice tracking during catheter navigation, complicating procedures and limiting catheter visibility. An automated slice tracking approach has been proposed for continuous catheter visualization but relies on operator/subject-specific inputs.

Goal(s): To develop and evaluate a real-time, parameter-free AI-based framework for continuous, automatic tracking of gadolinium-filled balloon catheters during MRI-guided cardiac catheterization.

Approach: A deep-learning framework with two imaging modes was tested on a 3D-printed phantom and pediatric patients, assessing accuracy, sensitivity, and specificity.

Results: The framework demonstrated high accuracy (>98%) and robustness across various anatomies and contrasts, showing feasibility for patient-independent tracking in MRI-guided cardiac procedures.

Impact: This AI-based framework could simplify MRI-guided cardiac catheterization by reducing manual input, potentially decreasing procedural time and enhancing visualization accuracy, supporting broader clinical adoption and improved patient outcomes.

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