mlsanityData to 7 Oct 2026

Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models

arXiv 2408.03156 ↗First posted 6 Aug 2024cs.CV cs.LG physics.med-phcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Image-generative artificial intelligence (AI) has garnered significant attention in recent years. In particular, the diffusion model, a core component of generative AI, produces high-quality images with rich diversity. In this study, we proposed a novel computed tomography (CT) reconstruction method by combining the denoising diffusion probabilistic model with iterative CT reconstruction. In sharp contrast to previous studies, we optimized the fidelity loss of CT reconstruction with respect to the latent variable of the diffusion model, instead of the image and model parameters. To suppress the changes in anatomical structures produced by the diffusion model, we shallowed the diffusion and reverse processes and fixed a set of added noises in the reverse process to make it deterministic during the inference. We demonstrated the effectiveness of the proposed method through the sparse-projection CT reconstruction of 1/10 projection data. Despite the simplicity of the implementation, the proposed method has the potential to reconstruct high-quality images while preserving the patient's anatomical structures and was found to outperform existing methods, including iterative reconstruction, iterative reconstruction with total variation, and the diffusion model alone in terms of quantitative indices such as the structural similarity index and peak signal-to-noise ratio. We also explored further sparse-projection CT reconstruction using 1/20 projection data with the same trained diffusion model. As the number of iterations increased, the image quality improved comparable to that of 1/10 sparse-projection CT reconstruction. In principle, this method can be widely applied not only to CT but also to other imaging modalities.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 33 of its 52 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

33 of 52 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model17 Jul
  2. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction14 Jun
  3. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction27 Apr
  4. 2024Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction30 Mar
  5. 2023Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT10 Oct
  6. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  7. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 26

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2023Consistency Modelscited by 1,778
  7. 2017Deep Image Priorcited by 1,097
  8. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  9. 2017Learned Primal-dual Reconstructioncited by 251
  10. 2017Image reconstruction by domain transform manifold learningcited by 231
  11. 2017Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Losscited by 161
  12. 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
14 more
  1. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  2. 2018Adversarial Regularizers in Inverse Problemscited by 102
  3. 2018NETT: Solving Inverse Problems with Deep Neural Networkscited by 89
  4. 2020FISTA-Net: Learning A Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imagingcited by 74
  5. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 66
  6. 2020Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methodscited by 56
  7. 2021DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoisingcited by 46
  8. 2018Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomographycited by 29
  9. 2017Sharpness-aware Low dose CT denoising using conditional generative adversarial networkcited by 28
  10. 2018Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiographycited by 23
  11. 2020Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoisingcited by 10
  12. 2024Inverse Problems with Diffusion Models: A MAP Estimation Perspectivecited by 9
  13. 2023Fast and Stable Diffusion Inverse Solver with History Gradient Updatecited by 8
  14. 2021Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversioncited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Priorcited by 3

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  1. 2026A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
  2. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction
  3. 2023Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
  4. 2023CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model
  5. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
  6. 2025Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction
  7. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Model
  8. 2023Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction