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-behaviorbased
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.