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.

翻译：皮层厚度测量的精度验证因缺乏真实数据而面临困难。为解决此问题，现有多种方法通过可变形配准在MRI中合成诱导灰质萎缩，从而生成具有已知皮层厚度变化的数据集。然而，这些方法常导致萎缩区域模糊，且无法在深沟回区域模拟逼真的萎缩——该区域脑脊液常被遮挡或缺失。本文提出一种解决方案：采用自监督填充模型生成这些区域的CSF，并创建具有更真实GM/CSF边界的图像。具体而言，我们引入新型3D GAN模型，融合基于图像块的丢失训练、边缘图先验及正弦位置编码技术——这些成熟方法此前仅局限于二维领域。实验表明，我们的框架显著提升了合成图像质量，并可通过微调适应未见数据。最后，我们证明生成数据集可用于皮层分割与厚度测量的精度验证。