Computed tomography-based radial endobronchial ultrasound image simulation of peripheral pulmonary lesions using deep learning.

BACKGROUND AND OBJECTIVES

Radial endobronchial ultrasound (R-EBUS) plays an important role during transbronchial sampling of peripheral pulmonary lesions (PPLs). However, existing navigational bronchoscopy systems provide no guidance for R-EBUS. To guide intraoperative R-EBUS probe manipulation, we aimed to simulate R-EBUS images of PPLs from preoperative computed tomography (CT) data using deep learning.

MATERIALS AND METHODS

Preoperative CT and intraoperative ultrasound data of PPLs in 250 patients who underwent R-EBUS-guided transbronchial lung biopsy were retrospectively collected. Two-dimensional CT sections perpendicular to the biopsy path were transformed into ultrasonic reflection and transmission images using an ultrasound propagation model to obtain the initial simulated R-EBUS images. A cycle generative adversarial network was trained to improve the realism of initial simulated images. Objective and subjective indicators were used to evaluate the similarity between real and simulated images.

RESULTS

Wasserstein distances showed that utilizing the cycle generative adversarial network significantly improved the similarity between real and simulated R-EBUS images. There was no statistically significant difference in the long axis, short axis, and area between real and simulated lesions (all > 0.05). Based on the experts' evaluation, a median similarity score of ≥4 on a 5-point scale was obtained for lesion size, shape, margin, internal echoes, and overall similarity.

CONCLUSIONS

Simulated R-EBUS images of PPLs generated by our method can closely mimic the corresponding real images, demonstrating the potential of our method to provide guidance for intraoperative R-EBUS probe manipulation.