mlsanityData to 7 Oct 2026

Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation

arXiv 2402.16212 ↗First posted 25 Feb 2024eess.IVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible method for super-resolution (SR), but sourcing paired training data that adequately models the target task is difficult. Additionally, SR algorithms can distort noise texture, which is an important in many clinical diagnostic scenarios. Here, we train conditional denoising diffusion probabilistic models (DDPMs) for PCCT super-resolution, with the objective to retain textural characteristics of local noise. PCCT simulation methods are used to synthesize realistic resolution degradation. To preserve noise texture, we explore decoupling the noise and signal image inputs and outputs via deep denoisers, explicitly mapping to each during the SR process. Our experimental results indicate that our DDPM trained on simulated data can improve sharpness in real PCCT images. Additionally, the disentanglement of noise from the original image allows our model more faithfully preserve noise texture.

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 13 of its 20 references that are in the map. First posted on arXiv 25 Feb 2024.

Builds on

13 of 20 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 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  4. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  5. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  7. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  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 81
  11. 2020X-ray Photon-Counting Data Correction through Deep Learningcited by 3

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Close in wording, no citation either way

  1. 20243D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
  2. 2026Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling
  3. 2025Noise Controlled CT Super-Resolution with Conditional Diffusion Model
  4. 2023PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models
  5. 2026CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
  6. 2025Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
  7. 2024Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data
  8. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction