In medical imaging, the diffusion models have shown great potential in synthetic image generation tasks. However, these models often struggle with the interpretable connections between the generated and existing images and could create illusions. To address these challenges, our research proposes a novel diffusion-based generative model based on deformation diffusion and recovery. This model, named Deformation-Recovery Diffusion Model (DRDM), diverges from traditional score/intensity and latent feature-based approaches, emphasizing morphological changes through deformation fields rather than direct image synthesis. This is achieved by introducing a topological-preserving deformation field generation method, which randomly samples and integrates a set of multi-scale Deformation Vector Fields (DVF). DRDM is trained to learn to recover unreasonable deformation components, thereby restoring each randomly deformed image to a realistic distribution. These innovations facilitate the generation of diverse and anatomically plausible deformations, enhancing data augmentation and synthesis for further analysis in downstream tasks, such as few-shot learning and image registration. Experimental results in cardiac MRI and pulmonary CT show DRDM is capable of creating diverse, large (over 10% image size deformation scale), and high-quality (negative ratio of folding rate is lower than 1%) deformation fields. The further experimental results in downstream tasks, 2D image segmentation and 3D image registration, indicate significant improvements resulting from DRDM, showcasing the potential of our model to advance image manipulation and synthesis in medical imaging and beyond. Our implementation will be available at https://github.com/jianqingzheng/def_diff_rec.

翻译：在医学影像领域，扩散模型在合成图像生成任务中展现出巨大潜力。然而，这些模型往往难以建立生成图像与现有图像之间的可解释关联，且可能产生伪影。为应对这些挑战，本研究提出了一种基于变形扩散与恢复的新型扩散生成模型。该模型命名为变形-恢复扩散模型（DRDM），区别于传统的基于分数/强度及潜在特征的方法，其通过变形场强调形态学变化而非直接图像合成。这一目标通过引入拓扑保持的变形场生成方法实现，该方法随机采样并整合一组多尺度变形矢量场（DVF）。DRDM通过训练学习恢复不合理变形分量，从而将每个随机变形图像还原至真实分布。这些创新机制促进了多样化且解剖学合理的变形生成，增强了数据增强与合成能力，为下游任务（如少样本学习与图像配准）的进一步分析提供支持。心脏MRI与肺部CT的实验结果表明，DRDM能够生成多样化、大尺度（变形幅度超过图像尺寸10%）且高质量（折叠率负值低于1%）的变形场。在下游任务（二维图像分割与三维图像配准）的进一步实验中，DRDM带来了显著性能提升，彰显了该模型在医学影像及其他领域推动图像操纵与合成技术发展的潜力。我们的实现代码将在https://github.com/jianqingzheng/def_diff_rec 公开。