Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Taofeng Xie, Zhuoxu Cui, Congcong Liu, Chen Luo, Huayu Wang +7 more
8/7/2024
cs.CVcs.AI

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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.

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Cite this paper

@article{xie2024joint,
  title  = {Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model},
  author = {Taofeng Xie and Zhuoxu Cui and Congcong Liu and Chen Luo and Huayu Wang and Yuanzhi Zhang and Xuemei Wang and Yihang Zhou and Qiyu Jin and Guoqing Chen and Dong Liang and Haifeng Wang},
  year   = {2024},
  eprint = {2408.11840},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.11840v1}
}

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