CT-CBCT deformable registration using weakly-supervised artifact-suppression transfer learning network

Abstract

Abstract Objective. Computed tomography-cone-beam computed tomography (CT-CBCT) deformable registration has great potential in adaptive radiotherapy. It plays an important role in tumor tracking, secondary planning, accurate irradiation, and the protection of at-risk organs. Neural networks have been improving CT-CBCT deformable registration, and almost all registration algorithms based on neural networks rely on the gray values of both CT and CBCT. The gray value is a key factor in the loss function, parameter training, and final efficacy of the registration. Unfortunately, the scattering artifacts in CBCT affect the gray values of different pixels inconsistently. Therefore, the direct registration of the original CT-CBCT introduces artifact superposition loss. Approach. In this study, a histogram analysis method for the gray values was used. Based on an analysis of the gray value distribution characteristics of different regions in CT and CBCT, the degree of superposition of the artifact in the region of disinterest was found to be much higher than that in the region of interest. Moreover, the former was the main reason for artifact superposition loss. Consequently, a new weakly supervised two-stage transfer-learning network based on artifact suppression was proposed. The first stage was a pre-training network designed to suppress artifacts contained in the region of disinterest. The second stage was a convolutional neural network that registered the suppressed CBCT and CT. Main Results. Through a comparative test of the thoracic CT-CBCT deformable registration, whose data were collected from the Elekta XVI system, the rationality and accuracy after artifact suppression were confirmed to be significantly improved compared with the other algorithms without artifact suppression. Significance. This study proposed and verified a new deformable registration method with multi-stage neural networks, which can effectively suppress artifacts and further improve registration by incorporating a pre-training technique and an attention mechanism.