mlsanityData to 7 Oct 2026

NERD: Network-Regularized Diffusion Sampling For 3D Computed Tomography

arXiv 2511.14680 ↗First posted 18 Nov 2025eess.IVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formulating sampling as an optimization procedure that enforces measurement consistency, forward diffusion consistency, and both step-wise and backward diffusion consistency. However, these methods have only considered 2D reconstruction tasks and do not directly extend to 3D image reconstruction problems, such as in Computed Tomography (CT). To bridge this gap, we propose NEtwork-Regularized diffusion sampling for 3D CT (NERD) by incorporating an L1 regularization into the optimization objective. This regularizer encourages spatial continuity across adjacent slices, reducing inter-slice artifacts and promoting coherent volumetric reconstructions. Additionally, we introduce two efficient optimization strategies to solve the resulting objective: one based on the Alternating Direction Method of Multipliers (ADMM) and another based on the Primal-Dual Hybrid Gradient (PDHG) method. Experiments on medical 3D CT data demonstrate that our approach achieves either state-of-the-art or highly competitive results.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 26 references that are in the map. First posted on arXiv 18 Nov 2025.

Builds on

15 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov

Other references · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  4. 2021Convex optimizationcited by 2,479
  5. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  6. 2017Deep Image Priorcited by 1,097
  7. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  8. 2017Compressed Sensing using Generative Modelscited by 337
  9. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  10. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  11. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  12. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
2 more
  1. 2024SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problemscited by 23
  2. 2022Gravitationally Lensed Black Hole Emission Tomographycited by 22

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  1. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
  2. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction
  3. 2026Improving 2D Diffusion Models for 3D Medical Imaging with Inter-Slice Consistent Stochasticity
  4. 2025Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction
  5. 2025Local Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction
  6. 2026Tracing the Oracle: Improving Diffusion Timestep Scheduling for 3D CT Reconstruction
  7. 2026From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priors
  8. 2023Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models