Universal Generative Modeling in Dual-domain for Dynamic MR Imaging

TL;DR

This paper introduces a universal dual-domain generative model for dynamic MRI reconstruction that leverages score-based priors and low-rank regularization, enabling high-quality, flexible, and efficient reconstruction from highly under-sampled data.

Contribution

The proposed DD-UGM uniquely combines score-based priors with low-rank regularization in a dual-domain framework, allowing single-frame training to reconstruct multiple frames effectively.

Findings

Enhanced noise reduction and detail preservation in reconstructions.

Ability to reconstruct different frames with a single trained model.

Demonstrated robustness and flexibility in dynamic MRI reconstruction.

Abstract

Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the reconstruction problem is still challenging due to its ill-posed nature. Recently, diffusion models espe-cially score-based generative models have exhibited great potential in algorithm robustness and usage flexi-bility. Moreover, the unified framework through the variance exploding stochastic differential equation (VE-SDE) is proposed to enable new sampling methods and further extend the capabilities of score-based gener-ative models. Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method. Much more than that, DD-UGM can reconstruct data of differ-ent frames by only training a single frame image, which reflects the flexibility of the proposed model.