In medical imaging, the diffusion models have shown great potential for synthetic image generation tasks. However, these approaches often lack the interpretable connections between the generated and real images and can create anatomically implausible structures or illusions. To address these limitations, we propose the Deformation-Recovery Diffusion Model (DRDM), a novel diffusion-based generative model that emphasises morphological transformation through deformation fields rather than direct image synthesis. DRDM introduces a topology-preserving deformation field generation strategy, which randomly samples and integrates multi-scale Deformation Velocity Fields (DVFs). DRDM is trained to learn to recover unrealistic deformation components, thus restoring randomly deformed images to a realistic distribution. This formulation enables the generation of diverse yet anatomically plausible deformations that preserve structural integrity, thereby improving data augmentation and synthesis for downstream tasks such as few-shot learning and image registration. Experiments on cardiac Magnetic Resonance Imaging and pulmonary Computed Tomography show that DRDM is capable of creating diverse, large-scale deformations, while maintaining anatomical plausibility of deformation fields. Additional evaluations on 2D image segmentation and 3D image registration tasks indicate notable performance gains, underscoring DRDM's potential to enhance both image manipulation and generative modelling in medical imaging applications. Project page: https://jianqingzheng.github.io/def_diff_rec/

翻译：在医学影像领域，扩散模型已展现出在合成图像生成任务中的巨大潜力。然而，这些方法往往缺乏生成图像与真实图像之间可解释的关联，且可能产生解剖学上不合理结构或伪影。为应对这些局限，我们提出了形变-恢复扩散模型（DRDM），这是一种基于扩散的新型生成模型，其核心在于通过形变场实现形态学变换，而非直接进行图像合成。DRDM引入了一种保持拓扑结构的形变场生成策略，该策略随机采样并融合多尺度形变速度场（DVFs）。DRDM通过学习恢复非真实的形变分量进行训练，从而将随机形变的图像还原至真实分布。这一机制能够生成多样且解剖学上合理的形变，同时保持结构完整性，从而提升了下游任务（如少样本学习和图像配准）中的数据增强与合成效果。在心脏磁共振成像和肺部计算机断层扫描上的实验表明，DRDM能够生成多样化、大尺度的形变，同时保持形变场的解剖学合理性。在2D图像分割和3D图像配准任务上的进一步评估显示出显著的性能提升，凸显了DRDM在增强医学影像应用中图像操控与生成建模能力方面的潜力。项目页面：https://jianqingzheng.github.io/def_diff_rec/