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

Dinglun He, Baoming Zhang, Xu Wang, Yao Hao, Deshan Yang, Ye Duan

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.

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

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.
Paper Structure (13 sections, 6 equations, 6 figures, 3 tables)

This paper contains 13 sections, 6 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Prior-Integrated Variation Modeling
  4. Sequential Slice-Wise Volume Generation
  5. Experiments and Results
  6. Dataset and Preparation
  7. Implementation Details
  8. Results
  9. Downstream Segmentation Task
  10. Ablation Study
  11. Conclusion
  12. Compliance with Ethical Standards
  13. ACKNOWLEDGMENTS

Figures (6)

  • Figure 1: Variation Modeling via Diffusion for CT Image Synthesis. The variation is forward diffused to Gaussian noise. A reverse diffusion model, guided by the segmentation label and average intensity map, predicts the variation signal, which is added back to reconstruct the final CT image.
  • Figure 2: Construction of Average Intensity Map and Variation. An average intensity map is computed from the segmentation label by replacing each organ region with its dataset-level mean value. The pixel-wise difference between the original CT image and the average map defines the variation used in subsequent diffusion modeling.
  • Figure 3: Examples of PIVM’s generation process. Columns show the ground-truth CT image, the corresponding average organ intensity priors, the predicted intensity variations, and the reconstructed CT obtained by combining the prior with the variation.
  • Figure 4: Zoomed-in comparison of synthesis quality. Columns display: (a) ground-truth CT, (b) segmentation labels, (c) CDDPM outputs, (d) MAISI outputs, and (e) PIVM outputs.
  • Figure 5: Multi-view visualization of a generated 3D abdominal CT volume, illustrating anatomical fidelity and inter-slice continuity. Left: coronal view with overlaid multi-organ contours. Right: sagittal view of the same volume.
  • ...and 1 more figures