mlsanityData to 7 Oct 2026

Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction

arXiv 2309.00853 ↗First posted 2 Sep 2023eess.IV cs.CVcited by 14

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Most existing MRI reconstruction methods perform tar-geted reconstruction of the entire MR image without tak-ing specific tissue regions into consideration. This may fail to emphasize the reconstruction accuracy on im-portant tissues for diagnosis. In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image. In addition, a diffusion process can converge more quickly if its target distribution closely resembles the noise distri-bution in the process. This can be accomplished through various high-frequency prior extractors. The finding further solidifies the effectiveness of the score-based gen-erative model. On top of all the advantages, our method improves the accuracy of MRI reconstruction and accel-erates sampling process. Experimental results verify that the proposed method successfully obtains more accurate reconstruction and outperforms state-of-the-art methods.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 51 references that are in the map. First posted on arXiv 2 Sep 2023.

Builds on

29 of 51 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI11 Apr
  2. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models6 Feb
  3. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  4. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  5. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction5 Mar
  6. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models3 Feb
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov
  8. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 21

  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. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  5. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  6. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 670
  7. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  8. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 306
  9. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  10. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusioncited by 230
  11. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  12. 2022Accelerating Diffusion Models via Early Stop of the Diffusion Processcited by 100
9 more
  1. 2022Score-based Generative Modeling Secretly Minimizes the Wasserstein Distancecited by 50
  2. 2021fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Datacited by 29
  3. 2020Homotopic Gradients of Generative Density Priors for MR Image Reconstructioncited by 14
  4. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
  5. 2022Accelerating Score-based Generative Models for High-Resolution Image Synthesiscited by 10
  6. 2019Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networkscited by 8
  7. 2021MRI Reconstruction Using Deep Energy-Based Modelcited by 8
  8. 2022WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstructioncited by 8
  9. 2023Geometry of Score Based Generative Modelscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 4; 2026: 1.

14 papers.

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By area:

  1. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  2. 2024Physics-Inspired Generative Models in Medical Imaging: A Reviewcited by 5
  3. 2023Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imagingcited by 4
  4. 2024DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Modelscited by 4
  5. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  6. 2024A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learningcited by 3
  7. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  8. 2024Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstructioncited by 2
  9. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  10. 2024MS-Glance: Bio-Insipred Non-semantic Context Vectors and their Applications in Supervising Image Reconstructioncited by 0
  11. 2025Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstructioncited by 0
  12. 2025Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstructioncited by 0
2 more
  1. 2025Diffusion-Assisted Frequency Attention Model for Whole-body Low-field MRI Reconstructioncited by 0
  2. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022High-Frequency Space Diffusion Models for Accelerated MRI
  2. 2026Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction
  3. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
  4. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  5. 2024Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI
  6. 2024MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
  7. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  8. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling