Coordinate-based fast lightweight path search algorithm for electromagnetic navigation bronchoscopy

• Published in: Medical & biological engineering & computing Vol. 61; no. 3; pp. 699 - 708
• Main Authors: Wu, Wenbin, Xia, Wei, Jun, Zhong, Saghatchi, Samaneh, Lavasani, Saeedeh Navaei, Mohagheghi, Saeed, Ahmadian, Alireza, Gao, Xin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; bronchi; Bronchoscopy; Bronchoscopy - methods; Computed tomography; Computer Applications; Consumption; Electromagnetic Phenomena; Euclidean geometry; High resolution; Human Physiology; Humans; Image acquisition; Image resolution; Imaging; Lightweight; Lung - pathology; Lung Neoplasms - pathology; Lungs; Medical imaging; memory; Original Article; Path planning; Radiology; Search algorithms; tomography; topology; Trachea; Electromagnetic navigation bronchoscopy; Path search; Path planning; Coordinate search; High-resolution computed tomography
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-022-02740-8

Electromagnetic navigation bronchoscopy (ENB) uses electromagnetic positioning technology to guide the bronchoscope to accurately and quickly reach the lesion along the planned path. However, enormous data in high-resolution lung computed tomography (CT) and the complex structure of multilevel branching bronchial tree make fast path search challenging for path planning. We propose a coordinate-based fast lightweight path search (CPS) algorithm for ENB. First, the centerline is extracted from the bronchial tree by applying topological thinning. Then, Euclidean-distance-based coordinate search is applied. The centerline points are represented by their coordinates, and adjacent points along the navigation path are selected considering the shortest Euclidean distance to the target on the centerline nearest the lesion. From the top of the trachea centerline, search is repeated until reaching the target. In 50 high-resolution lung CT images acquired from five scanners, the CPS algorithm achieves accuracy, average search time, and average memory consumption of 100%, 88.5 ms, and 166.0 MB, respectively, reducing search time by 74.3% and 73.1% and memory consumption by 83.3% and 83.0% compared with Dijkstra and A* algorithms, respectively. CPS algorithm is suitable for path search in multilevel branching bronchial tree navigation based on high-resolution lung CT images. Graphical Abstract