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

TL;DR

This paper introduces a novel MRI reconstruction method that leverages multi-frequency priors and diffusion processes to enhance image quality and speed, especially focusing on important tissues for diagnosis.

Contribution

The proposed scheme uniquely combines multi-frequency prior mining with diffusion models to improve MRI reconstruction accuracy and accelerate the sampling process.

Findings

Achieves more accurate MRI reconstructions than state-of-the-art methods.

Successfully emphasizes important tissue regions in reconstructed images.

Speeds up the diffusion-based sampling process.

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.