  Deformable registration of magnetic resonance images between patients with
brain tumors and healthy subjects has been an important tool to specify tumor
geometry through location alignment and facilitate pathological analysis. Since
tumor region does not match with any ordinary brain tissue, it has been
difficult to deformably register a patients brain to a normal one. Many patient
images are associated with irregularly distributed lesions, resulting in
further distortion of normal tissue structures and complicating registration's
similarity measure. In this work, we follow a multi-step context-aware image
inpainting framework to generate synthetic tissue intensities in the tumor
region. The coarse image-to-image translation is applied to make a rough
inference of the missing parts. Then, a feature-level patch-match refinement
module is applied to refine the details by modeling the semantic relevance
between patch-wise features. A symmetry constraint reflecting a large degree of
anatomical symmetry in the brain is further proposed to achieve better
structure understanding. Deformable registration is applied between inpainted
patient images and normal brains, and the resulting deformation field is
eventually used to deform original patient data for the final alignment. The
method was applied to the Multimodal Brain Tumor Segmentation (BraTS) 2018
challenge database and compared against three existing inpainting methods. The
proposed method yielded results with increased peak signal-to-noise ratio,
structural similarity index, inception score, and reduced L1 error, leading to
successful patient-to-normal brain image registration.
