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

• URL: http://arxiv.org/abs/2506.18270v1
• Date: Mon, 23 Jun 2025 03:54:53 GMT
• Title: Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction
• Authors: Qinrong Cai, Yu Guan, Zhibo Chen, Dong Liang, Qiuyun Fan, Qiegen Liu,
• Abstract summary: Masked modeling has emerged as a distinctive approach that involves predicting parts of the original data that are proportionally masked during training.<n>This work introduces a diffusion model based on adaptive masks (AMDM), which utilizes the adaptive adjustment of frequency distribution based on k-space data.<n> Experimental results verified the ability of this method to learn specific frequency information and thereby improved the quality of MRI reconstruction.
• Score: 19.96167625441933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation [33.99418884128739]
  We introduce a Frequency-enhanced Multi-granularity Context Network (FMC-Net) to improve vertebrae segmentation accuracy.<n>For the high-frequency components, we apply a High-frequency Feature Refinement (HFR) to amplify the prominence of key features.<n>For the low-frequency components, we use a Multi-granularity State Space Model (MG-SSM) to aggregate feature representations with different receptive fields.
  arXiv  Detail & Related papers  (2025-06-29T04:53:02Z)
• Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model [58.694932010573346]
  Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis.<n>We propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images.
  arXiv  Detail & Related papers  (2024-12-10T15:25:18Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation [16.63720411275398]
  This study presents a generalized implicit neural representation (INR)-based framework for MRI PI reconstruction.<n>The proposed method overcomes limitations by leveraging prior knowledge of voxel-specific features.<n>Experiments on publicly available MRI datasets demonstrate the superior performance of the proposed method.
  arXiv  Detail & Related papers  (2023-09-12T09:07:03Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction [3.083408283778178]
  This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors.
  arXiv  Detail & Related papers  (2023-09-02T01:33:50Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction [8.44194619347218]
  Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space. We propose a method that directly learns the undersampling masks from data points. We show that different anatomic regions reveal distinct optimal undersampling masks.
  arXiv  Detail & Related papers  (2023-05-25T14:42:04Z)
• Masked Frequency Modeling for Self-Supervised Visual Pre-Training [102.89756957704138]
  We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. MFM first masks out a portion of frequency components of the input image and then predicts the missing frequencies on the frequency spectrum. For the first time, MFM demonstrates that, for both ViT and CNN, a simple non-Siamese framework can learn meaningful representations even using none of the following: (i) extra data, (ii) extra model, (iii) mask token.
  arXiv  Detail & Related papers  (2022-06-15T17:58:30Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Deep MRI Reconstruction with Radial Subsampling [2.7998963147546148]
  Retrospectively applying a subsampling mask onto the k-space data is a way of simulating the accelerated acquisition of k-space data in real clinical setting. We compare and provide a review for the effect of applying either rectilinear or radial retrospective subsampling on the quality of the reconstructions outputted by trained deep neural networks.
  arXiv  Detail & Related papers  (2021-08-17T17:45:51Z)
• Multi-Modal MRI Reconstruction with Spatial Alignment Network [51.74078260367654]
  In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study. Recent researches demonstrate that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence. In this paper, we integrate the spatial alignment network with reconstruction, to improve the quality of the reconstructed target modality.
  arXiv  Detail & Related papers  (2021-08-12T08:46:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.