Xu Di, Xu Sheng, Herzka Daniel A, Yung Rex C, Bergtholdt Martin, Gutierrez Luis F, McVeigh Elliot R
Dept. of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3715-8. doi: 10.1109/IEMBS.2010.5627657.
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.
在X线引导下的周围型肺病变支气管镜检查中，气道和结节在X线图像中很难看清。周围病变的经支气管活检通常是盲目进行的，导致诊断率降低。解决这个问题的一种方法是将从术前3D CT图像分割出的病变和气道叠加到2D X线图像上。提出了一种基于特征的2D/3D配准方法，用于两种成像模态数据集之间的图像融合。该算法使用两幅立体X线图像来提高配准的准确性和鲁棒性。算法从CT图像和X线图像中提取骨结构的边缘特征。根据X线图像边缘点的图像梯度方向信息将其分为八组。为每组X线特征点生成一个方向相关的欧几里得距离图。然后将距离图应用于投影CT图像的边缘点，其梯度方向与距离图兼容。在满足方向约束后，通过将投影CT分割的边界与X线图像的最近边缘进行匹配来配准CT图像和X线图像。进行了体模和临床研究以验证算法的性能，结果显示配准精度为4.19（±0.5）mm，配准时间为48.39（±9.6）秒。该算法也在临床数据上进行了评估，显示出了有前景的配准精度和鲁棒性。
