Registration is one of the fundamental tasks in medical image analysis. It is an essential step for many applications to establish spatial correspondences between two images. However, image registration in the presence of pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. For example, for patients with brain tumors, the tissue is often displaced by the tumors, creating more significant deformations than what is observed in a healthy brain. Moreover, a fast and accurate image registration in the presence of pathologies is especially desired for immediate assessment of the registration results. This dissertation addresses the following problems concerning the registration of images with pathologies: (1) efficient registration between an image with pathologies and a common control atlas; (2) patient-specific longitudinal registration between pre-operative and post recurrence images for patients with glioblastoma; (3) automatic brain extraction for images with pathologies; and (4) fast predictive registration of images with pathologies to an atlas. Contributions presented in this dissertation are as follows: (1) I develop a joint PCA/image-reconstruction approach for images with pathologies. The model estimates quasi-normal image appearance from the image with pathologies and uses the reconstructed quasi normal image for registration. It improves the registration accuracy compared to directly using the images with pathologies, while not requiring the segmentation of the pathological region. (2) I propose a patient-specific registration framework for the longitudinal study of tumor recurrence of patients diagnosed with glioblastoma. It models the healthy tissue appearance for each patient in the individual space, thereby improving the registration accuracy. (3) I develop a brain extraction method for images with pathologies by jointly modeling healthy brain tissue, pathologies, and non-brain volume. (4) I design a joint registration and reconstruction deep learning model which learns an appearance mapping from the image with pathologies to atlas appearance while simultaneously predicting the transformation to atlas space. The network disentangles the spatial variation from the appearance changes caused by the pathology.