Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores.

翻译：生物相容性材料3D打印技术的进步使得患者特定植入物日益普及。然而，这些植入物的设计仍然是一个繁琐且主要依赖人工的过程。现有自动化植入物生成的方法主要基于对降采样或分块数据应用的3D U-Net架构，这可能导致细节或上下文信息的丢失。受扩散概率模型近期成功的启发，我们提出了一种基于3D点云扩散模型与体素化网络相结合的新型植入物生成方法。由于扩散模型中的随机采样过程，我们可以针对每个缺损区域生成一组不同的植入物候选方案，医生可从中选择最合适的方案。我们在SkullBreak和SkullFix数据集上评估了该方法，生成了高质量的植入物，并取得了具有竞争力的评估分数。