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Initially,\n                      <jats:italic>Calibration<\/jats:italic>\n                      mode identifies the catheter tip's three\u2010dimensional coordinates using a fixed stack of contiguous slices. A U\u2010Net architecture with a ResNet\u201034 encoder is used to identify the catheter tip location. Once identified, the sequence then switches to\n                      <jats:italic>Runtime<\/jats:italic>\n                      mode, dynamically acquiring three contiguous slices automatically centered on the catheter tip. The catheter location is estimated from each\n                      <jats:italic>Runtime<\/jats:italic>\n                      stack using the same network and fed back to the sequence, enabling prospective slice tracking to keep the catheter in the central slice. If the catheter remains unidentified over several dynamics, the sequence reverts to\n                      <jats:italic>Calibration<\/jats:italic>\n                      mode. This artificial intelligence (AI)\u2013based approach was evaluated prospectively in a three\u2010dimensional\u2010printed heart phantom and 3 patients undergoing MR\u2010guided cardiac catheterization. This technique was also compared retrospectively in 2 patients with a previous non\u2010AI automatic tracking method relying on operator\u2010defined parameters.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the phantom study, the tracking framework achieved 100% accuracy\/sensitivity\/specificity in both modes. Across all patients, the average accuracy\/sensitivity\/specificity were 100\u2009\u00b1\u20090\/100\u2009\u00b1\u20090\/100\u2009\u00b1\u20090% (\n                      <jats:italic>Calibration<\/jats:italic>\n                      ) and 98.4\u2009\u00b1\u20090.8\/94.1\u2009\u00b1\u20092.9\/100.0\u2009\u00b1\u20090.0% (\n                      <jats:italic>Runtime<\/jats:italic>\n                      ). The parametric, non\u2010AI technique and the proposed parameter\u2010free AI\u2010based framework yielded identical accuracy (100%) in\n                      <jats:italic>Calibration<\/jats:italic>\n                      mode and similar accuracy range in\n                      <jats:italic>Runtime<\/jats:italic>\n                      mode (Patients 1 and 2: 100%\u201397%, and 100%\u201398%, respectively).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An AI\u2010based prospective slice\u2010tracking framework was developed for real\u2010time, parameter\u2010free, operator\u2010independent, automatic tracking of gadolinium\u2010filled balloon catheters. Its feasibility was successfully demonstrated in patients undergoing MRI\u2010guided cardiac catheterization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.30574","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T01:54:18Z","timestamp":1749434058000},"page":"1626-1634","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning\u2013based prospective slice tracking for continuous catheter visualization during\n                    <scp>MRI<\/scp>\n                    \u2010guided cardiac catheterization"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8488-3925","authenticated-orcid":false,"given":"Alexander Paul","family":"Neofytou","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine King's College London  London 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