Joint Distribution Modeling for Accelerated T1rho Reconstruction

Traditional hand-craft designed methods to accelerated T1rho mapping have limited characterisation capabilities, while deep learning methods lack the interpretability. On the other hand, the joint distribution is the most direct and accurate way to characterize the correlation between different images. Therefore, we attempt to propose a joint distribution estimation method and use it to construct a T1$ρ$ reconstruction model. In particular, we use a score-based diffusion model to model the joint distribution of acquired T1rho-weighted images. Moreover, the corresponding reconstruction model is solved using the Langevin gradient descent method. Finally, numerical experiments validate the effectiveness of the proposed method.