用解剖先验生成精准腹部CT，解决数据少与隐私问题
PIVM: Diffusion-Based Prior-Integrated Variation Modeling for Anatomically Precise Abdominal CT Synthesis
- 基于扩散模型，以器官先验引导体素强度变化
- 保持完整亨氏单位范围，还原细微解剖纹理
- 适合医学影像合成与数据增强，尤其关注腹部
腹部CT数据受限于高标注成本和隐私约束，制约了分割与诊断模型的发展。我们提出一种基于扩散的解剖先验集成变分建模框架（PIVM），实现解剖精确的腹部CT图像合成。该方法不从噪声生成完整图像，而是基于器官特异性强度先验，预测相对于先验的体素级强度变化。这些先验与分割标签共同引导扩散过程，确保空间对齐与真实器官边界。相比潜在空间扩散模型，本方法直接在图像空间操作，保留完整的亨氏单位（HU）范围，无需平滑即可捕捉精细解剖纹理。源代码已开源：https://github.com/BZNR3/PIVM。
原文摘要 · Abstract (English)
Abdominal CT data are limited by high annotation costs and privacy constraints, which hinder the development of robust segmentation and diagnostic models. We present a Prior-Integrated Variation Modeling (PIVM) framework, a diffusion-based method for anatomically accurate CT image synthesis. Instead of generating full images from noise, PIVM predicts voxel-wise intensity variations relative to organ-specific intensity priors derived from segmentation labels. These priors and labels jointly guide the diffusion process, ensuring spatial alignment and realistic organ boundaries. Unlike latent-space diffusion models, our approach operates directly in image space while preserving the full Hounsfield Unit (HU) range, capturing fine anatomical textures without smoothing. Source code is available at https://github.com/BZNR3/PIVM.
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