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

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.

Dictionary learning technique enhances signal in LED-based photoacoustic imaging.

There has been growing interest in low-cost light sources such as light-emitting diodes (LEDs) as an excitation source in photoacoustic imaging. However, LED-based photoacoustic imaging is limited by low signal due to low energy per pulse-the signal is easily buried in noise leading to low quality images. Here, we describe a signal de-noising approach for LED-based photoacoustic signals based on dictionary learning with an alternating direction method of multipliers. This signal enhancement method is then followed by a simple reconstruction approach delay and sum. This approach leads to sparse representation of the main components of the signal. The main improvements of this approach are a 38% higher contrast ratio and a 43% higher axial resolution versus the averaging method but with only 4% of the frames and consequently 49.5% less computational time. This makes it an appropriate option for real-time LED-based photoacoustic imaging.