mlsanityData to 7 Oct 2026

Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT

arXiv 2410.21301 ↗First posted 21 Oct 2024eess.IV cs.AI cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer concentrated or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation properties. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 31 of its 44 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

31 of 44 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction9 May
  2. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction27 Apr
  3. 2023Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction28 Aug
  4. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction25 Nov
  5. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction22 Nov
  6. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction5 Mar
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 24

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2022Classifier-Free Diffusion Guidancecited by 5,717
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  8. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  9. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  10. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  11. 2018Demystifying MMD GANscited by 1,268
  12. 2021Variational Diffusion Modelscited by 1,145
12 more
  1. 2022Denoising Diffusion Restoration Modelscited by 951
  2. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  3. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  4. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  5. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  6. 2023Rethinking FID: Towards a Better Evaluation Metric for Image Generationcited by 303
  7. 2021SNIPS: Solving Noisy Inverse Problems Stochasticallycited by 191
  8. 2019Effectively Unbiased FID and Inception Score and where to find themcited by 161
  9. 2020TorchRadon: Fast Differentiable Routines for Computed Tomographycited by 44
  10. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  11. 2023Learning to Distill Global Representation for Sparse-View CTcited by 14
  12. 2024Bayesian Conditioned Diffusion Models for Inverse Problemscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026A Gibbs posterior sampler for inverse problem based on prior diffusion modelcited by 1
  2. 2025Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributionscited by 0

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  3. 2025When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
  4. 2023CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model
  5. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
  6. 2026Stochastic Generative Plug-and-Play Priors
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