PIVM: Diffusion-Based Prior-Integrated Variation Modeling for Anatomically Precise Abdominal CT Synthesis

• URL: http://arxiv.org/abs/2603.22626v1
• Date: Mon, 23 Mar 2026 22:56:48 GMT
• Title: PIVM: Diffusion-Based Prior-Integrated Variation Modeling for Anatomically Precise Abdominal CT Synthesis
• Abstract summary: We present a diffusion-based method for anatomically accurate CT image synthesis.<n>Instead of generating full images from noise, PIVM predicts voxel-wise intensity variations relative to organ-specific intensity priors.<n>These priors and labels jointly guide the diffusion process, ensuring spatial alignment and realistic organ boundaries.
• Score: 6.978452372711662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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