The scarcity of medical images poses major challenges for downstream tasks and AI deployment. While generative models like VAEs, GANs, and diffusion models offer potential solutions, they often fail to capture the fine-grained anatomical details essential in medical imaging, particularly for scoliosis. This highlights the need for a specialized model that preserves structural and lesion-specific features. We propose A-PriDiff, a diffusion-based generative model tailored for ultrasound scoliosis imaging. It adopts a two-phase framework: a pre-trained segmentation U-Net extracts anatomical features, which are refined by an Anatomical Prior Encoding Module (APEM) to guide a diffusion module in synthesizing high-fidelity images. Tested on a dataset of 1,170 raw and 109 segmented ultrasound images, A-PriDiff achieves superior performance (FID: 9.69, KID: 0.013), outperforming DDPM and GAN baselines. It also yields the lowest mean curve angle error (1.80 ± 0.90 \(^\circ \) ). Ablation results confirm APEM’s critical role in enhancing anatomical consistency.