Diffusion generative prior-based highly accelerated MR T1ρ mapping

Diffusion-based generative models have been applied to solve the inverse problem of MR reconstruction and show impressive results. However, the diffusion model requires many iterations to produce high-quality samples, prolonging the reconstruction time. It also may lead to stochastic differential equation (SDE) sequence divergence in MR reconstruction and degrades the reconstruction quality. We proposed a new SDE for diffusion-based MR reconstruction that focuses on the diffusion process in high-frequency of k-space to improve reconstruction robustness and reduce the iterations. We applied the proposed method in MR T1ρ mapping, showing that it can achieve a high acceleration of 14X.