Explicit-implicit symmetric diffeomorphic deformable image registration with convolutional neural network

Medical image registration is essential and a key step in many advanced medical image tasks. In recent years, medical image registration has been applied to many clinical diagnoses, but large deformation registration is still a challenge. Deep learning-based methods typically have higher accuracy but do not involve spatial transformation, which ignores some desirable properties, including topology preservation and the invertibility of transformation, for medical imaging studies. On the other hand, diffeomorphic registration methods achieve a differentiable spatial transformation, which guarantees topology preservation and invertibility of transformation, but registration accuracy is low. Therefore, a diffeomorphic deformation registration with CNN is proposed, based on a symmetric architecture, simultaneously estimating forward and inverse deformation fields. CNN with Efficient Channel Attention is used to better capture the spatial relationship. Deformation fields are optimized explicitly and implicitly to enhance the invertibility of transformations. An extensive experimental evaluation is performed using two 3D datasets. The proposed method is compared with different state-of-the-art methods. The experimental results show excellent registration accuracy while better guaranteeing the diffeomorphic transformation. To achieve a smoother deformation field, we combine the use of smoothness regularization with a reconstruction approach, then propose an explicit-implicit diffeomorphic deformable image registration model based on a symmetric architecture. image