2D/3D registration for X-ray guided bronchoscopy using distance map classification

In X-ray guided bronchoscopy of peripheral pulmonary lesions, airways and nodules are hardly visible in X-ray images. Transbronchial biopsy of peripheral lesions is often carried out blindly, resulting in degraded diagnostic yield. One solution of this problem is to superimpose the lesions and airways segmented from preoperative 3D CT images onto 2D X-ray images. A feature-based 2D/3D registration method is proposed for the image fusion between the datasets of the two imaging modalities. Two stereo X-ray images are used in the algorithm to improve the accuracy and robustness of the registration. The algorithm extracts the edge features of the bony structures from both CT and X-ray images. The edge points from the X-ray images are categorized into eight groups based on the orientation information of their image gradients. An orientation dependent Euclidean distance map is generated for each group of X-ray feature points. The distance map is then applied to the edge points of the projected CT images whose gradient orientations are compatible with the distance map. The CT and X-ray images are registered by matching the boundaries of the projected CT segmentations to the closest edges of the X-ray images after the orientation constraint is satisfied. Phantom and clinical studies were carried out to validate the algorithm's performance, showing a registration accuracy of 4.19(± 0.5) mm with 48.39(± 9.6) seconds registration time. The algorithm was also evaluated on clinical data, showing promising registration accuracy and robustness.