Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction

• URL: http://arxiv.org/abs/2309.00853v1
• Date: Sat, 2 Sep 2023 07:51:27 GMT
• Title: Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
• Authors: Yu Guan, Chuanming Yu, Shiyu Lu, Zhuoxu Cui, Dong Liang, Qiegen Liu
• Abstract summary: Most existing MRI reconstruction methods perform tar-geted reconstruction of the entire MR image without considering specific tissue regions. 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.
• Score: 14.687337090732036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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