mlsanityData to 7 Oct 2026

Universal Generative Modeling in Dual-domain for Dynamic MR Imaging

arXiv 2212.07599 ↗First posted 15 Dec 2022eess.IV cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the reconstruction problem is still challenging due to its ill-posed nature. Recently, diffusion models espe-cially score-based generative models have exhibited great potential in algorithm robustness and usage flexi-bility. Moreover, the unified framework through the variance exploding stochastic differential equation (VE-SDE) is proposed to enable new sampling methods and further extend the capabilities of score-based gener-ative models. Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method. Much more than that, DD-UGM can reconstruct data of differ-ent frames by only training a single frame image, which reflects the flexibility of the proposed model.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 43 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

16 of 43 references.

References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  8. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  9. 2017Image reconstruction by domain transform manifold learningcited by 231
  10. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  11. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
  12. 2017Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MRcited by 31
4 more
  1. 2018DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Trainingcited by 26
  2. 2018A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstructioncited by 11
  3. 2022K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstructioncited by 4
  4. 2022Universal Generative Modeling for Calibration-free Parallel Mr Imagingcited by 2

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  3. 2024Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors
  4. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
  5. 2026Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
  6. 2024Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model
  7. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion model
  8. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data