Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
This is an incremental improvement for medical imaging, specifically MRI reconstruction, by enhancing accuracy in diagnostic tissues.
The paper tackles MRI reconstruction by focusing on important tissue regions using a multi-frequency diffusion model, resulting in more accurate reconstructions and faster sampling compared to state-of-the-art methods.
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.