Boundary-aware registration network for 4D-CT lung image with sliding motion

Background and Objective: Fast and accurate registration of 4D-CT lung images is significant for respiratory motion modeling and radiotherapy planning. However, since the complex respiratory motion involves sliding motion at lung boundary, the traditional registration methods and regularization terms perform poorly in recovering both sliding and smooth deformation in 4D-CT lung image registration.Methods: In order to overcome these limitations of the traditional registration methods and regularization terms, we propose a boundary-aware registration model with a spatially adaptive regularization term. We incorporate a lung segmentation network into the registration model. With the lung boundary-aware information from the segmentation network, we construct a spatially adaptive regularization term, which integrates the smooth regularization and the non-smooth regularization, to accommodate both smooth and sliding motion.Results: We evaluate the proposed registration model on clinical 4D-CT data and the public DIR-Lab dataset. Our model provides a minimum Target Registration Error (1.59 & PLUSMN; 0.57 mm) of landmarks compared with the other lung registration methods. The ablation studies show that the proposed spatially adaptive regularization term provides superior performance in HD (13.75 & PLUSMN; 3.36 mm) and MHD (1.63 & PLUSMN; 0.32 mm) to the smooth regularization term and non-smooth regularization term.Conclusions: The proposed boundary-aware registration model enables adaptive regularization term, which can flexibly regulate both the sliding motion at the lung boundary and the smooth motion inside the lung simultaneously. Therefore, our model can perform fast and accurate registration for 4D-CT lung images with sliding motion, which is beneficial to respiratory motion modeling and lung cancer radiotherapy.