Modified Hybrid Bronchoscope Tracking Based on Sequential Monte Carlo Sampler: Dynamic Phantom Validation

Abstract

This paper presents a new hybrid bronchoscope tracking method that uses an electromagnetic position sensor, a sequential Monte Carlo sampler, and its evaluation on a dynamic motion phantom. Since airway deformation resulting from patient movement, respiratory motion, and coughing can significantly affect the rigid registration between electromagnetic tracking and computed tomography (CT) coordinate systems, a standard hybrid tracking approach that initializes intensity-based image registration with absolute pose data acquired by electromagnetic tracking fails when the initial camera pose is too far from the actual pose. We propose a new solution that combines electromagnetic tracking and a sequential Monte Carlo sampler to address this problem. In our solution, sequential Monte Carlo sampling is introduced to recursively approximate the posterior probability distributions of the bronchoscope camera motion parameters in accordance with the observation model based on electromagnetic tracking. We constructed a dynamic phantom that simulates airway deformation to evaluate our proposed solution. Experimental results demonstrate that the challenging problem of airway deformation can be robustly modeled and effectively addressed with our proposed approach compared to a previous hybrid method, even when the maximum simulated airway deformation reaches 23 mm.