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.
@article{arxiv.2309.00853,
title = {Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction},
author = {Yu Guan and Chuanming Yu and Shiyu Lu and Zhuoxu Cui and Dong Liang and Qiegen Liu},
journal= {arXiv preprint arXiv:2309.00853},
year = {2023}
}