Abstract
Recent image registration methods built upon unsupervised learning have achieved promising diffeomorphic image registration performance. However, the bijective consistency of spatial transformations is not sufficiently investigated in existing image registration studies. In this study, we develop a multi-level image registration framework to achieve diffeomorphic image registration in a coarse-to-fine manner. A novel stationary velocity field computation method is proposed to integrate forward and inverse stationary velocity fields so that the image registration result is invariant to the order of input images to be registered. Moreover, a new bijective consistency regularization is adopted to enforce the bijective consistency of forward and inverse transformations at different time points along the stationary velocity integration paths. Validation experiments have been conducted on two T1-weighted magnetic resonance imaging (MRI) brain datasets with manually annotated anatomical structures. Compared with four state-of-the-art representative diffeomorphic registration methods, including two traditional diffeomorphic registration algorithms and two unsupervised learning-based diffeomorphic registration approaches, our method has achieved better image registration accuracy with superior topology preserving performance.