  Recently, diffusion models have gained significant attention as a novel set
of deep learning-based generative methods. These models attempt to sample data
from a Gaussian distribution that adheres to a target distribution, and have
been successfully adapted to the reconstruction of MRI data. However, as an
unconditional generative model, the diffusion model typically disrupts image
coordination because of the consistent projection of data introduced by
conditional bootstrap. This often results in image fragmentation and
incoherence. Furthermore, the inherent limitations of the diffusion model often
lead to excessive smoothing of the generated images. In the same vein, some
deep learning-based models often suffer from poor generalization performance,
meaning their effectiveness is greatly affected by different acceleration
factors. To address these challenges, we propose a novel diffusion model-based
MRI reconstruction method, named TC-DiffRecon, which does not rely on a
specific acceleration factor for training. We also suggest the incorporation of
the MF-UNet module, designed to enhance the quality of MRI images generated by
the model while mitigating the over-smoothing issue to a certain extent. During
the image generation sampling process, we employ a novel TCKG module and a
Coarse-to-Fine sampling scheme. These additions aim to harmonize image texture,
expedite the sampling process, while achieving data consistency. Our source
code is available at https://github.com/JustlfC03/TC-DiffRecon.
