Abstract
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.
Abstract (translated)
监测影响大脑结构完整性的疾病需要对磁共振（MR）图像进行自动分析，例如，用于评估体积变化。然而，许多评估工具是针对分析健康组织优化的。因此，为了评估包含病理性组织的扫描，需要恢复病理性区域的健康组织。在这项工作中，我们探讨并扩展了用于一致去噪的扩散模型来修复健康3D脑组织。我们修改了在图像空间中工作的最先进的2D、伪3D和3D方法，以及3D潜在和3D波浪扩散模型，并将它们训练为合成健康脑组织。我们的评估显示，伪3D模型在结构相似性指数、峰值信号噪声比和均方误差方面表现最佳。为了强调临床相关性，我们在包含合成MS病变的数据上对 this模型进行微调，并将其在下游脑组织分割任务上进行评估。结果表明，该伪3D模型在病理性区域填充的现有FSL方法之上表现出色。
URL
https://arxiv.org/abs/2403.14499
