  Most existing MRI reconstruction methods perform tar-geted reconstruction of
the entire MR image without tak-ing specific tissue regions into consideration.
This may fail to emphasize the reconstruction accuracy on im-portant tissues
for diagnosis. In this study, leveraging a combination of the properties of
k-space data and the diffusion process, our novel scheme focuses on mining the
multi-frequency prior with different strategies to pre-serve fine texture
details in the reconstructed image. In addition, a diffusion process can
converge more quickly if its target distribution closely resembles the noise
distri-bution in the process. This can be accomplished through various
high-frequency prior extractors. The finding further solidifies the
effectiveness of the score-based gen-erative model. On top of all the
advantages, our method improves the accuracy of MRI reconstruction and
accel-erates sampling process. Experimental results verify that the proposed
method successfully obtains more accurate reconstruction and outperforms
state-of-the-art methods.

Image and Video Processing;Computer Vision and Pattern Recognition