Deep learning–based prospective slice tracking for continuous catheter visualization during MRI ‐guided cardiac catheterization

作者：Alexander Neofytou, Grzegorz Kowalik, Rohini Vidya Shankar, Karl Kunze, Tracy Moon, Nina Mellor, Radhouène Neji, Reza Razavi, Kuberan Pushparajah, Sébastien Roujol · 发表于：Magnetic Resonance in Medicine · 年份：2025 · DOI：10.1002/mrm.30574 · 被引用次数：5 · 研究领域：Advanced Radiotherapy Techniques、Medical Image Segmentation Techniques、Soft Robotics and Applications

PURPOSE: This proof-of-concept study introduces a novel, deep learning-based, parameter-free, automatic slice-tracking technique for continuous catheter tracking and visualization during MR-guided cardiac catheterization. METHODS: The proposed sequence includes Calibration and Runtime modes. Initially, Calibration mode identifies the catheter tip's three-dimensional coordinates using a fixed stack of contiguous slices. A U-Net architecture with a ResNet-34 encoder is used to identify the catheter tip location. Once identified, the sequence then switches to Runtime mode, dynamically acquiring three contiguous slices automatically centered on the catheter tip. The catheter location is estimated from each Runtime 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 Calibration mode. This artificial intelligence (AI)-based approach was evaluated prospectively in a three-dimensional-printed heart phantom and 3 patients undergoing MR-guided cardiac catheterization. This technique was also compared retrospectively in 2 patients with a previous non-AI automatic tracking method relying on operator-defined parameters. RESULTS: 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 ± 0/100 ± 0/10...