This paper is a contribution to the "BraTS 2023 Local Synthesis of Healthy Brain Tissue via Inpainting Challenge". The task of this challenge is to transform tumor tissue into healthy tissue in brain magnetic resonance (MR) images. This idea originates from the problem that MR images can be evaluated using automatic processing tools, however, many of these tools are optimized for the analysis of healthy tissue. By solving the given inpainting task, we enable the automatic analysis of images featuring lesions, and further downstream tasks. Our approach builds on denoising diffusion probabilistic models. We use a 2D model that is trained using slices in which healthy tissue was cropped out and is learned to be inpainted again. This allows us to use the ground truth healthy tissue during training. In the sampling stage, we replace the slices containing diseased tissue in the original 3D volume with the slices containing the healthy tissue inpainting. With our approach, we achieve comparable results to the competing methods. On the validation set our model achieves a mean SSIM of 0.7804, a PSNR of 20.3525 and a MSE of 0.0113. In future we plan to extend our 2D model to a 3D model, allowing to inpaint the region of interest as a whole without losing context information of neighboring slices.

通过使用去噪扩散概率模型，本文介绍了一种把肿瘤组织转化为健康组织的方法，通过自动分析磁共振图像中的异常组织，进而实现下游任务。我们的方法在验证集上取得了可比较的结果，平均结构相似性指数为0.7804，峰值信噪比为20.3525，均方误差为0.0113。未来将扩展这种方法，使其能够整体修复感兴趣区域而不会丢失邻近切片的上下文信息。

健康脑组织修复的去噪扩散模型