Image registration is a fundamental requirement for many medical applications. In recent years, deep learning approaches for registration have shown to be a promising alternative to conventional methods. However, most learning based methods do not consider the different physical properties of various tissues, which can result in unrealistic deformation in anatomical regions where both deformable tissue and rigid bone is present. In this work, we develop and evaluate deep learning methods for intrapatient CT-MR registration while maintaining rigidity of the bones. Unconstrained and locally constrained registration methods are compared in an unsupervised and weakly-supervised setting. The results show that qualitatively and quantitatively accurate registrations can be obtained.