mlsanityData to 14 Oct 2026

Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction

arXiv 2309.00783 ↗First posted 2 Sep 2023cs.LGcited by 22

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy and efficiency for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 45 references that are in the map. First posted on arXiv 2 Sep 2023.

Builds on

20 of 45 references.

Closest ancestors · 7

Same direction, newest first

  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey14 Nov
  2. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Models17 Jul
  3. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  4. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar
  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. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  7. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  8. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contractioncited by 307
  9. 2017Image reconstruction by domain transform manifold learningcited by 232
  10. 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
  11. 2018DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Trainingcited by 26
  12. 2021An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivitiescited by 10
1 more
  1. 2021MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mappingcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 9; 2025: 10; 2026: 3.

22 papers.

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  1. 2024Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approachcited by 24
  2. 2024Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generationcited by 17
  3. 2025Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classificationcited by 10
  4. 2024Style Transfer: From Stitching to Neural Networkscited by 8
  5. 2024G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solvingcited by 4
  6. 2025On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstructioncited by 4
  7. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  8. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
  9. 2025EquiReg: Equivariance Regularized Diffusion for Inverse Problemscited by 3
  10. 2024MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstructioncited by 2
  11. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  12. 2025Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Modelscited by 2
10 more
  1. 2025Physics informed guided diffusion for accelerated multi-parametric MRI reconstructioncited by 2
  2. 2024Guided MRI Reconstruction via Schr"odinger Bridgecited by 1
  3. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  4. 2025MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstructioncited by 1
  5. 2025q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Modelscited by 1
  6. 2026Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mappingcited by 1
  7. 2025Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectorscited by 0
  8. 2025Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimizationcited by 0
  9. 2026Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRIcited by 0
  10. 2026Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  2. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  3. 2023Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise
  4. 2026Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
  5. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
  6. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
  7. 2022High-Frequency Space Diffusion Models for Accelerated MRI
  8. 2025Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction