A markerless beam's eye view tumor tracking algorithm based on structure conversion and demons registration in medical image / 中华放射肿瘤学杂志

ABSTRACT

Objective:To propose a markerless beam's eye view (BEV) tumor tracking algorithm, which can be applied to megavolt (MV) images with poor image quality, multi-leaf collimator (MLC) occlusion and non-rigid deformation.Methods:Window template matching, image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images. The quality assurance (QA) plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator, and 682 electronic portal imaging device (EPID) images in the treatment process were collected as fixed images. Meanwhile, the digitally reconstructured radiograph (DRR) images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm. In addition, a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study, providing quantitative results of tumor location changes during treatment. Image similarity was used for third-party verification of tracking results.Results:The algorithm could cope with different degrees (10%-80%) of image missing. In the phantom verification, 86.8% of the tracking errors were less than 3 mm, and 80% were less than 2 mm. Normalized mutual information (NMI) varied from 1.182±0.026 to 1.202±0.027 ( P<0.005) before and after registration and the change of Hausdorff distance (HD) was from 57.767±6.474 to 56.664±6.733 ( P<0.005). The case results were predominantly translational (-6.0 mm to 6.2 mm), but non-rigid deformation still existed. NMI varied from 1.216±0.031 to 1.225±0.031 ( P<0.005) before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089 ( P<0.005). Conclusions:The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies. It provides a reference idea for processing MV images with multi-modality, partial data and poor image quality.