Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e.g., for the evaluation of volumetric changes. However, many of the evaluation tools are optimized for analyzing healthy tissue. To enable the evaluation of scans containing pathological tissue, it is therefore required to restore healthy tissue in the pathological areas. In this work, we explore and extend denoising diffusion models for consistent inpainting of healthy 3D brain tissue. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error. To emphasize the clinical relevance, we fine-tune this model on data containing synthetic MS lesions and evaluate it on a downstream brain tissue segmentation task, whereby it outperforms the established FMRIB Software Library (FSL) lesion-filling method.

翻译：监测影响脑结构完整性的疾病需要对磁共振（MR）图像进行自动化分析，例如评估体积变化。然而，许多评估工具针对健康组织分析而优化。为评估含有病理组织的扫描图像，需在病理区域恢复健康组织。本研究探索并拓展了基于去噪扩散模型的一致性3D健康脑组织修复方法。我们改进了当前最先进的2D、伪3D及3D图像空间方法，以及3D潜在空间和3D小波扩散模型，并训练它们合成健康脑组织。评估结果显示，伪3D模型在结构相似性指数、峰值信噪比和均方误差指标上表现最优。为突出临床相关性，我们对含有合成多发性硬化病灶的数据进行该模型的微调，并在下游脑组织分割任务中评估其性能，结果表明其优于成熟的FMRIB软件库（FSL）病灶填充方法。