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Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction

arXiv 2506.18270 ↗First posted 23 Jun 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

As the deep learning revolution marches on, masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training, and has demonstrated exceptional performance in multiple fields. Magnetic Resonance Imaging (MRI) reconstruction is a critical task in medical imaging that seeks to recover high-quality images from under-sampled k-space data. However, previous MRI reconstruction strategies usually optimized the entire image domain or k-space, without considering the importance of different frequency regions in the k-space This work introduces a diffusion model based on adaptive masks (AMDM), which utilizes the adaptive adjustment of frequency distribution based on k-space data to develop a hybrid masks mechanism that adapts to different k-space inputs. This enables the effective separation of high-frequency and low-frequency components, producing diverse frequency-specific representations. Additionally, the k-space frequency distribution informs the generation of adaptive masks, which, in turn, guide a closed-loop diffusion process. Experimental results verified the ability of this method to learn specific frequency information and thereby improved the quality of MRI reconstruction, providing a flexible framework for optimizing k-space data using masks in the future.

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References · 34

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  3. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
  4. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  5. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  6. 2024Simplified and Generalized Masked Diffusion for Discrete Datacited by 626
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 521
  8. 2021Score-based diffusion models for accelerated MRIcited by 311
  9. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  10. 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 244
  11. 2017Image reconstruction by domain transform manifold learningcited by 232
  12. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
22 more
  1. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imagingcited by 127
  2. 2020Generative Modeling with Denoising Auto-Encoders and Langevin Samplingcited by 119
  3. 2025Masked Autoencoders Are Effective Tokenizers for Diffusion Modelscited by 96
  4. 2022Subspace Diffusion Generative Modelscited by 70
  5. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 65
  6. 2019Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisitioncited by 56
  7. 2023Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learningcited by 56
  8. 2022Masked Image Modeling with Denoising Contrastcited by 45
  9. 2021WINNet: Wavelet-inspired Invertible Network for Image Denoisingcited by 34
  10. 2021fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Datacited by 29
  11. 2024Masked Image Modeling: A Surveycited by 29
  12. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstructioncited by 27
  13. 2022DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removalcited by 25
  14. 2024VideoMAC: Video Masked Autoencoders Meet ConvNetscited by 20
  15. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstructioncited by 14
  16. 2023Masked Modeling for Self-supervised Representation Learning on Vision and Beyondcited by 13
  17. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 12
  18. 2022WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstructioncited by 8
  19. 2024DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Modelscited by 4
  20. 2023Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Studycited by 3
  21. 2024Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstructioncited by 2
  22. 2022Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstructioncited by 1

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  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. 2024Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
  7. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  8. 2023CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?