  Accuracy validation of cortical thickness measurement is a difficult problem
due to the lack of ground truth data. To address this need, many methods have
been developed to synthetically induce gray matter (GM) atrophy in an MRI via
deformable registration, creating a set of images with known changes in
cortical thickness. However, these methods often cause blurring in atrophied
regions, and cannot simulate realistic atrophy within deep sulci where
cerebrospinal fluid (CSF) is obscured or absent. In this paper, we present a
solution using a self-supervised inpainting model to generate CSF in these
regions and create images with more plausible GM/CSF boundaries. Specifically,
we introduce a novel, 3D GAN model that incorporates patch-based dropout
training, edge map priors, and sinusoidal positional encoding, all of which are
established methods previously limited to 2D domains. We show that our
framework significantly improves the quality of the resulting synthetic images
and is adaptable to unseen data with fine-tuning. We also demonstrate that our
resulting dataset can be employed for accuracy validation of cortical
segmentation and thickness measurement.
