Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
This addresses the problem of enhancing image quality and speed in PET-MRI systems for medical imaging applications, representing an incremental advancement in joint reconstruction techniques.
The paper tackled the challenge of low signal-to-noise ratio in PET and slow acquisition in MRI by proposing a joint reconstruction model using diffusion stochastic differential equations, which improved PET and MRI reconstruction quality and surpassed state-of-the-art methods.
PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming by PET-MRI systems. We aim to accelerate MRI and improve PET image quality. This paper proposed a novel joint reconstruction model by diffusion stochastic differential equations based on learning the joint probability distribution of PET and MRI. Compare the results underscore the qualitative and quantitative improvements our model brings to PET and MRI reconstruction, surpassing the current state-of-the-art methodologies. Joint PET-MRI reconstruction is a challenge in the PET-MRI system. This studies focused on the relationship extends beyond edges. In this study, PET is generated from MRI by learning joint probability distribution as the relationship.