Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

TL;DR

This paper introduces a novel joint PET-MRI reconstruction method using diffusion stochastic differential equations to enhance image quality and accelerate MRI, outperforming existing techniques.

Contribution

The study proposes a new diffusion SDE-based model that learns the joint probability distribution of PET and MRI for improved reconstruction quality.

Findings

Significant qualitative improvements in PET and MRI images.

Quantitative metrics show superior reconstruction accuracy.

Model accelerates MRI data acquisition process.

Abstract

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.