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Related Experiment Video

Updated: Sep 19, 2025

Magnetically-Assisted Remote Controlled Microcatheter Tip Deflection under Magnetic Resonance Imaging
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Deep learning-based prospective slice tracking for continuous catheter visualization during MRI-guided cardiac

Alexander Paul Neofytou1, Grzegorz Kowalik1, Rohini Vidya Shankar1

  • 1School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

Magnetic Resonance in Medicine
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

A novel artificial intelligence (AI) deep learning technique enables automatic, parameter-free catheter tracking during MR-guided cardiac catheterization. This AI-based method demonstrated high accuracy and feasibility in phantom and patient studies.

Keywords:
MR guidancecardiac catheterizationdeep learningpassive trackingreal time

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Interventions

Background:

  • MR-guided cardiac catheterization requires precise catheter tracking for accurate navigation.
  • Current tracking methods can be operator-dependent and require manual parameter adjustments.
  • A need exists for automated, real-time catheter tracking solutions.

Purpose of the Study:

  • To introduce a novel, deep learning-based, parameter-free, automatic slice-tracking technique.
  • To enable continuous catheter tracking and visualization during MR-guided cardiac catheterization.
  • To evaluate the feasibility and performance of this AI-based approach.

Main Methods:

  • A U-Net architecture with a ResNet-34 encoder was employed for catheter tip identification.
  • The technique utilizes Calibration and Runtime modes for initial localization and dynamic tracking.
  • Prospective evaluation was conducted in a heart phantom and 3 patients, with retrospective comparison to a non-AI method.

Main Results:

  • The AI tracking framework achieved 100% accuracy, sensitivity, and specificity in phantom studies.
  • In patient studies, average accuracy/sensitivity/specificity were 100%/100%/100% (Calibration) and 98.4%/94.1%/100% (Runtime).
  • The AI method showed comparable or superior performance to a previous non-AI tracking technique.

Conclusions:

  • An AI-based prospective slice-tracking framework was successfully developed.
  • The technique provides real-time, parameter-free, operator-independent catheter tracking.
  • Feasibility was demonstrated in patients undergoing MRI-guided cardiac catheterization.