Deep learning–based prospective slice tracking for continuous catheter visualization during <scp>MRI</scp> ‐guided cardiac catheterization
An AI-based prospective slice-tracking framework was developed for real-time, parameter-free, operator-independent, automatic tracking of gadolinium-filled balloon catheters. Its feasibility was successfully demonstrated in patients undergoing MRI-guided cardiac catheterization.
Authors
- Alexander Neofytou (ORCID: https://orcid.org/0000-0002-8488-3925)
- Reza Razavi (ORCID: https://orcid.org/0000-0003-1065-3008)
- Kuberan Pushparajah (ORCID: https://orcid.org/0000-0003-1541-1155)
- Karl Kunze (ORCID: https://orcid.org/0000-0001-6403-2712)
- Rohini Vidya Shankar (ORCID: https://orcid.org/0000-0003-0762-7052)
- Radhouène Neji (ORCID: https://orcid.org/0000-0002-6543-4390)
- Sébastien Roujol (ORCID: https://orcid.org/0000-0002-7146-0552)
- Grzegorz Kowalik (ORCID: https://orcid.org/0000-0003-3267-8652)
- Nina Mellor
- Tracy Moon
Institutions
- King's College London (GB)
- Siemens (United Kingdom) (GB)
- Evelina London Children's Healthcare (GB)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-06-08
- DOI
- https://doi.org/10.1002/mrm.30574
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- King’s College London
- National Institute for Health and Care Research
- British Heart Foundation
- King's College London
- Siemens Healthineers
- Engineering and Physical Sciences Research Council
- Centre For Medical Engineering, King’s College London