  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.
