An Interventionalist-Behavior-Based Data Fusion Framework for Guidewire Tracking in Percutaneous Coronary Intervention

Guidewire tracking is a clinical challenge in percutaneous coronary intervention (PCI). The current practice of image-based and sensor-based tracking techniques is still limited by radiation exposure, contrast injection, device sterilization, and procedure safety. In this paper, an interventionalist-behavior-based data fusion framework is developed to provide a novel strategy for tracking guidewire motions in PCI. Four types of natural behavior were acquired from ten interventionalists while performing guidewire translation and rotation based on a simulation platform. Different numbers of behaviors are fused by a hierarchical framework with six local tracking models and three ensemble algorithms. After Gaussian mixture regression-based ensemble fusion, a three-behavior scheme can achieve average tracking errors of 1.07 ± 0.17 mm for guidewire translation, and 20.05 ± 3.36° for guidewire rotation. Relevant statistical analysis further reveals that this scheme outperforms the cases using fewer behaviors, and ensemble fusion brings significant error reduction compared with only local fusion. These meaningful results indicate the great potential of the proposed framework for promoting the improvement of guidewire tracking in PCI.