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:由于缺乏地面真实数据,对皮层厚度测量的准确性验证是一个困难的问题。为了解决这一需要,已经开发了许多方法,通过可变登记,合成地诱发磁共振成磁共振的灰物质(GM)萎缩,产生一系列已知皮层厚度变化的图像;然而,这些方法往往在萎缩地区造成模糊,无法模拟深硫中现实的萎缩,因为脑骨质素(CSF)模糊或缺失。在本文中,我们提出了一个解决方案,使用自监督的喷漆模型模型在这些区域生成 CSF,并在GM/CSF边界上创建更可信的图像。具体地说,我们引入了一个新的3DGAN模型,其中包括基于补丁的辍学训练、边缘图的前期和正弦性定位编码,所有这些方法以前都限于2D区域。我们表明,我们的框架大大改进了由此产生的合成图像的质量,并适应了不见得的数据。我们还表明,我们产生的数据集可用于对皮层断裂分层和厚度测量的精确性校准。</s>