Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Autor:	Xie, Taofeng, Cui, Zhuoxu, Liu, Congcong, Luo, Chen, Wang, Huayu, Zhang, Yuanzhi, Wang, Xuemei, Zhou, Yihang, Jin, Qiyu, Chen, Guoqing, Liang, Dong, Wang, Haifeng
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Zdroj:	ISMRM 2024 Digital poster 6575
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as ISMRM 2024 Digital poster 6575. 04-09 May 2024 Singapore
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.11840 Zobrazit plný text záznamu View this record from Arxiv