Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping

Yikang Liu, Lin Zhao, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun

TL;DR

The paper tackles dynamic coronary roadmapping, where overlaying an offline coronary roadmap onto live fluoroscopy requires accurate cardiac phase matching and catheter tip tracking. It introduces Auxiliary Input in Training (AIT), a simple method that injects catheter masks as an auxiliary input during training and gradually ablates it, enabling end-to-end models to leverage catheter information for improved representation learning. The authors implement AIT in two architectures for cardiac phase matching (CNN-C and CNN-T) and two for catheter tip tracking, pairing them with triplet and $L_1$ losses, and show substantial performance gains over baselines such as FT, MTL, and teacher-student approaches. The results demonstrate faster inference suitable for clinical use and indicate that catheter features can meaningfully enhance roadmap accuracy, suggesting potential to reduce reliance on ECG-based cues and lessen patient exposure during interventional procedures.

Abstract

Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray fluoroscopy in real-time. It aims to offer navigational guidance for interventional surgeries without the need for repeated contrast agent injections, thereby reducing the risks associated with radiation exposure and kidney failure. The precision of the roadmaps is contingent upon the accurate alignment of angiographic and fluoroscopic images based on their cardiac phases, as well as precise catheter tip tracking. The former ensures the selection of a roadmap that closely matches the vessel shape in the current frame, while the latter uses catheter tips as reference points to adjust for translational motion between the roadmap and the present vessel tree. Training deep learning models for both tasks is challenging and underexplored. However, incorporating catheter features into the models could offer substantial benefits, given humans heavily rely on catheters to complete the tasks. To this end, we introduce a simple but effective method, auxiliary input in training (AIT), and demonstrate that it enhances model performance across both tasks, outperforming baseline methods in knowledge incorporation and transfer learning.

Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping

TL;DR

The paper tackles dynamic coronary roadmapping, where overlaying an offline coronary roadmap onto live fluoroscopy requires accurate cardiac phase matching and catheter tip tracking. It introduces Auxiliary Input in Training (AIT), a simple method that injects catheter masks as an auxiliary input during training and gradually ablates it, enabling end-to-end models to leverage catheter information for improved representation learning. The authors implement AIT in two architectures for cardiac phase matching (CNN-C and CNN-T) and two for catheter tip tracking, pairing them with triplet and losses, and show substantial performance gains over baselines such as FT, MTL, and teacher-student approaches. The results demonstrate faster inference suitable for clinical use and indicate that catheter features can meaningfully enhance roadmap accuracy, suggesting potential to reduce reliance on ECG-based cues and lessen patient exposure during interventional procedures.

Abstract

Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray fluoroscopy in real-time. It aims to offer navigational guidance for interventional surgeries without the need for repeated contrast agent injections, thereby reducing the risks associated with radiation exposure and kidney failure. The precision of the roadmaps is contingent upon the accurate alignment of angiographic and fluoroscopic images based on their cardiac phases, as well as precise catheter tip tracking. The former ensures the selection of a roadmap that closely matches the vessel shape in the current frame, while the latter uses catheter tips as reference points to adjust for translational motion between the roadmap and the present vessel tree. Training deep learning models for both tasks is challenging and underexplored. However, incorporating catheter features into the models could offer substantial benefits, given humans heavily rely on catheters to complete the tasks. To this end, we introduce a simple but effective method, auxiliary input in training (AIT), and demonstrate that it enhances model performance across both tasks, outperforming baseline methods in knowledge incorporation and transfer learning.
Paper Structure (15 sections, 2 equations, 4 figures, 3 tables)

This paper contains 15 sections, 2 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. AIT
  4. Cardiac Phase Matching
  5. Catheter Tip Tracking
  6. Experiments
  7. Baseline Methods
  8. Ablation Studies
  9. Datasets and Evaluation Metrics
  10. Results and Discussion
  11. Conclusion
  12. Disclosure of Interests.
  13. Network Architectures
  14. CNN-ConvLSTM
  15. CNN-Transformer

Figures (4)

  • Figure 1: The Workflow of Dynamic Coronary Roadmapping.
  • Figure 2: Visualization of Features Learned by Different Methods in the CPM task. First row (left to right): original image and AIT ($\alpha$=0, 0.5, 0.8, 1). Second row (left to right): vanilla supervised learning, FT (before and after fine-tuning), MTL, and T-S.
  • Figure 3: Ablation Study
  • Figure S1: Architectures of CNN-ConvLSTM Networks. The left is the model for cardiac phase matching, and the right is for catheter tip tracking. Numbers above tensors are channel numbers.