This paper targets the challenges of scarce 3D medical imaging samples and insufficient structural consistency in kidney disease scenarios, and proposes a structure-aware 3D diffusion generation framework. In the forward diffusion stage, an organ-mask-guided adaptive noise scheduling mechanism is introduced to slow the degradation of critical structures; in the reverse denoising stage, a topology-prior conditional injection strategy is employed by fusing distance fields, boundary cues, and skeleton information to enhance connectivity and contour stability. Experiments on a public 3D renal MRI dataset demonstrate that, compared with a baseline diffusion model without structural priors, the proposed method achieves consistent improvements in generation quality: FID decreases from 18.74 to 11.16, KID decreases from 7.983 to 4.573, while PSNR increases from 26.214 to 28.577 and LPIPS decreases from 13.442 to 5.7543. Ablation studies further verify the complementarity of the two types of structural constraints: introducing either noise scheduling or topological priors alone yields stable gains, whereas their combination leads to a more substantial overall improvement. Moreover, under a transfer setting of "training on generated data and testing on real data," using synthetic samples for pre-training/augmentation effectively improves the cross-domain robustness of downstream segmentation, indicating that the generated 3D data are highly usable and practically valuable in terms of structural morphology and intensity distribution.
Xia et al. (Sun,) studied this question.