A robust framework with knowledge-guided planning and fiducial-based registration for bronchoscopy navigation system.

Objective: Bronchoscopy is a valuable minimally invasive examination in clinical practice and is widely used in the diagnosis of suspected peripheral lung lesions. However, this device has difficulty in accessing peripheral areas from lack of inadequate guidance and heavily relies on intraoperative X-ray or computerized tomography (CT) scan.

Approach: In order to overcome these limitations, we propose a robust navigation framework with knowledge-guided planning and fiducial-based registration for bronchoscopy navigation, which makes three notable contributions that have been experimentally verified to be of practical value. Firstly, we propose a preoperative path-planning algorithm with anatomical prior knowledge to generate a feasible and accurate trajectory. Secondly, a fiducial-based patient-image registration scheme is introduced to align virtual images with the patient's actual anatomy, building a robust rigid relationship and enabling real-time updates. Thirdly, we establish a position sensing approach to present precise positions and track the movement of the bronchoscope.

Main results: Extensive experiments on the 3D-printed airway tree model and in vivo porcine lung are conducted to evaluate comprehensive capabilities. Qualitative and quantitative results manifest that our framework can achieve excellent performance, reaching a success rate of 100% in the path-planning stage, achieving robust registration precision with a fiducial registration error (FRE) of 0.998 ± 0.074 mm, and obtaining the standard deviation of 0.017 mm, 0.206 mm and 0.013 mm in the tracking stage.

Significance: Our results demonstrate the feasibility and effectiveness and further has potential prospects as an auxiliary tool to extend the capabilities of clinical bronchoscopy.