Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Alexander Paul Neofytou; Grzegorz Tomasz Kowalik; Rohini Vidya Shankar; Li Huang; Tracy Moon; Nina Mellor; Reza Razavi; Radhouene Neji; Kuberan Pushparajah; Sébastien Roujol

doi:10.3389/fcvm.2023.1233093

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Front Cardiovasc Med. 2023 Sep 7:10:1233093. doi: 10.3389/fcvm.2023.1233093. eCollection 2023.

Authors

Affiliations

¹ School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
² Department of Paediatric Cardiology, Evelina London Children's Hospital, London, United Kingdom.
³ MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom.

Abstract

Introduction: Magnetic Resonance Imaging (MRI) is a promising alternative to standard x-ray fluoroscopy for the guidance of cardiac catheterization procedures as it enables soft tissue visualization, avoids ionizing radiation and provides improved hemodynamic data. MRI-guided cardiac catheterization procedures currently require frequent manual tracking of the imaging plane during navigation to follow the tip of a gadolinium-filled balloon wedge catheter, which unnecessarily prolongs and complicates the procedures. Therefore, real-time automatic image-based detection of the catheter balloon has the potential to improve catheter visualization and navigation through automatic slice tracking.

Methods: In this study, an automatic, parameter-free, deep-learning-based post-processing pipeline was developed for real-time detection of the catheter balloon. A U-Net architecture with a ResNet-34 encoder was trained on semi-artificial images for the segmentation of the catheter balloon. Post-processing steps were implemented to guarantee a unique estimate of the catheter tip coordinates. This approach was evaluated retrospectively in 7 patients (6M and 1F, age = 7 ± 5 year) who underwent an MRI-guided right heart catheterization procedure with all images acquired in an orientation unseen during training.

Results: The overall accuracy, specificity and sensitivity of the proposed catheter tracking strategy over all 7 patients were 98.4 ± 2.0%, 99.9 ± 0.2% and 95.4 ± 5.5%, respectively. The computation time of the deep-learning-based segmentation step was ∼10 ms/image, indicating its compatibility with real-time constraints.

Conclusion: Deep-learning-based catheter balloon tracking is feasible, accurate, parameter-free, and compatible with real-time conditions. Online integration of the technique and its evaluation in a larger patient cohort are now warranted to determine its benefit during MRI-guided cardiac catheterization.

Keywords: AI; MRI-guidance; cardiac catheterization; device tracking; real-time.

Grants and funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant (EP/R010935/1), the EPSRC Doctoral Training Partnership (DTP) grant (EP/R513064/1), the British Heart Foundation (BHF) (PG/19/11/34243 and PG/21/10539), the Wellcome EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z), the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, and Siemens Healthineers. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.