Real-time automatic image-based slice tracking of gadolinium-filled balloon wedge catheter during MR-guided cardiac catheterization: A proof-of-concept study.

PURPOSE: MR-guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium-filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real-time catheter tracking during MR-guided cardiac catheterization is presented. METHODS: The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10-20 contiguous saturated slices combined with real-time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR-guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated. RESULTS: The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real-time measurements in the phantom and patients, respectively. CONCLUSION: The proposed framework enabled real-time continuous automatic tracking of a gadolinium-filled catheter balloon during MR-guided cardiac catheterization.

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

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.