mlsanityData to 14 Oct 2026

3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model

arXiv 2408.15283 ↗First posted 22 Aug 2024cs.CV eess.SPcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This study aims to improve photon counting CT (PCCT) image resolution using denoising diffusion probabilistic models (DDPM). Although DDPMs have shown superior performance when applied to various computer vision tasks, their effectiveness has yet to be translated to high dimensional CT super-resolution. To train DDPMs in a conditional sampling manner, we first leverage CatSim to simulate realistic lower resolution PCCT images from high-resolution CT scans. Since maximizing DDPM performance is time-consuming for both inference and training, especially on high-dimensional PCCT data, we explore both 2D and 3D networks for conditional DDPM and apply methods to accelerate training. In particular, we decompose the 3D task into efficient 2D DDPMs and design a joint 2D inference in the reverse diffusion process that synergizes 2D results of all three dimensions to make the final 3D prediction. Experimental results show that our DDPM achieves improved results versus baseline reference models in recovering high-frequency structures, suggesting that a framework based on realistic simulation and DDPM shows promise for improving PCCT resolution.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 13 of its 18 references that are in the map. First posted on arXiv 22 Aug 2024.

Builds on

13 of 18 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction18 Nov
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  4. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  5. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  7. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 508
  8. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  9. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
  10. 2018CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)cited by 82
  11. 2020X-ray Photon-Counting Data Correction through Deep Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Foundation Models for Medical Imaging: Status, Challenges, and Directionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
  2. 2026Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling
  3. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
  4. 2023PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models
  5. 2025Noise Controlled CT Super-Resolution with Conditional Diffusion Model
  6. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedup
  7. 2023DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
  8. 2026CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution