K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without Noise

• URL: http://arxiv.org/abs/2311.10162v2
• Date: Fri, 16 Feb 2024 18:08:30 GMT
• Title: K-space Cold Diffusion: Learning to Reconstruct Accelerated MRI without Noise
• Authors: Guoyao Shen, Mengyu Li, Chad W. Farris, Stephan Anderson, Xin Zhang
• Abstract summary: We propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise. Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.
• Score: 2.982793366290863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based MRI reconstruction models have achieved superior performance these days. Most recently, diffusion models have shown remarkable performance in image generation, in-painting, super-resolution, image editing and more. As a generalized diffusion model, cold diffusion further broadens the scope and considers models built around arbitrary image transformations such as blurring, down-sampling, etc. In this paper, we propose a k-space cold diffusion model that performs image degradation and restoration in k-space without the need for Gaussian noise. We provide comparisons with multiple deep learning-based MRI reconstruction models and perform tests on a well-known large open-source MRI dataset. Our results show that this novel way of performing degradation can generate high-quality reconstruction images for accelerated MRI.

Related papers

• LDPM: Towards undersampled MRI reconstruction with MR-VAE and Latent Diffusion Prior [2.3007720628527104]
  A Latent Diffusion Prior based undersampled MRI reconstruction (LDPM) method is proposed. A sketcher module is utilized to provide appropriate control and balance the quality and fidelity of the reconstructed MR images. A VAE adapted for MRI tasks (MR-VAE) is explored, which can serve as the backbone for future MR-related tasks.
  arXiv  Detail & Related papers  (2024-11-05T09:51:59Z)
• Improved Patch Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting [7.379135816468852]
  Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI. achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions. We propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction.
  arXiv  Detail & Related papers  (2024-10-29T21:38:54Z)
• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models [2.5412006057370893]
  Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems. Our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks.
  arXiv  Detail & Related papers  (2024-07-03T01:37:56Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• 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)
• Self-Supervised MRI Reconstruction with Unrolled Diffusion Models [27.143473617162304]
  We propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon) SSDiffRecon expresses a conditional diffusion process that interleaves cross-attention transformers for reverse diffusion steps with data-consistency blocks for physics-driven processing. Experiments on public brain MR datasets demonstrate the superiority of SSDiffRecon against state-of-the-art supervised, and self-supervised baselines in terms of reconstruction speed and quality.
  arXiv  Detail & Related papers  (2023-06-29T03:31:46Z)
• 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)
• Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models [75.52575380824051]
  We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
  arXiv  Detail & Related papers  (2023-06-05T22:09:06Z)
• Towards performant and reliable undersampled MR reconstruction via diffusion model sampling [67.73698021297022]
  DiffuseRecon is a novel diffusion model-based MR reconstruction method. It guides the generation process based on the observed signals. It does not require additional training on specific acceleration factors.
  arXiv  Detail & Related papers  (2022-03-08T02:25:38Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)

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.