Score-based diffusion models for accelerated MRI

@article{Chung2022ScorebasedDM,
  title={Score-based diffusion models for accelerated MRI},
  author={Hyungjin Chung and Jong-Chul Ye},
  journal={ArXiv},
  year={2022},
  volume={abs/2110.05243}
}
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5 Citations
Background Citations
2
Methods Citations
4

5 Citations

Citation Type

Citation Type

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This work proposes a new denoising method based on score-based reverse diffusion sampling, which overcomes all the aforementioned drawbacks and establishes state-of-the-art performance, while having desirable properties which prior MMSE denoisers did not have.
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TLDR
The proposed DiffuseRecon achieves SoTA performances reconstructing from raw acquisition signals in fastMRI and SKM-TEA, and proposes an accelerated, coarse-to-fine Monte-Carlo sampling scheme to approximate the most likely reconstruction candidate.
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
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Experimental results demonstrate that WKGM can attain state-of-the-art reconstruction results under the well-learned k-space generative prior, and is flexible and thus can synergistically combine various traditional k- space PI models, generating learning-based priors to produce high-fidelity reconstructions.

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