mlsanityData to 13 Oct 2026

Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction

arXiv 2308.14437 ↗First posted 28 Aug 2023cs.CV cs.AIcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Score-based generative models (SGMs) have gained prominence in sparse-view CT reconstruction for their precise sampling of complex distributions. In SGM-based reconstruction, data consistency in the score-based diffusion model ensures close adherence of generated samples to observed data distribution, crucial for improving image quality. Shortcomings in data consistency characterization manifest in three aspects. Firstly, data from the optimization process can lead to artifacts in reconstructed images. Secondly, it often neglects that the generation model and original data constraints are independently completed, fragmenting unity. Thirdly, it predominantly focuses on constraining intermediate results in the inverse sampling process, rather than ideal real images. Thus, we propose an iterative optimization data scoring model. This paper introduces the data-iterative optimization score-based model (DOSM), integrating innovative data consistency into the Stochastic Differential Equation, a valuable constraint for ultra-sparse-view CT reconstruction. The novelty of this data consistency element lies in its sole reliance on original measurement data to confine generation outcomes, effectively balancing measurement data and generative model constraints. Additionally, we pioneer an inference strategy that traces back from current iteration results to ideal truth, enhancing reconstruction stability. We leverage conventional iteration techniques to optimize DOSM updates. Quantitative and qualitative results from 23 views of numerical and clinical cardiac datasets demonstrate DOSM's superiority over other methods. Remarkably, even with 10 views, our method achieves excellent performance.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 23 of its 36 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

23 of 36 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems16 Aug
  2. 2023Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models15 Mar
  3. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction25 Nov
  4. 2022Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models19 Nov
  5. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction18 Nov
  6. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  2. 2020Denoising Diffusion Implicit Modelscited by 9,718
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,028
  4. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,077
  5. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,023
  6. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,403
  7. 2022Denoising Diffusion Restoration Modelscited by 958
  8. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 508
  9. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 432
  10. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 307
  11. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  12. 2017Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CTcited by 87
4 more
  1. 2022DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removalcited by 25
  2. 2020Improving Robustness of Deep-Learning-Based Image Reconstructioncited by 23
  3. 2021Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstructioncited by 15
  4. 2017Total Variation Superiorized Conjugate Gradient Method for Image Reconstructioncited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstructioncited by 4
  2. 2024Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CTcited by 2
  3. 2025Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusioncited by 1

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  3. 2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models
  4. 2025Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction
  5. 2025ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction
  6. 2025Ordered-subsets Multi-diffusion Model for Sparse-view CT Reconstruction
  7. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
  8. 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates