[2408.11840] Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2408.11840 (cs)
[Submitted on 7 Aug 2024]

Title:Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Authors:Taofeng Xie, Zhuoxu Cui, Congcong Liu, Chen Luo, Huayu Wang, Yuanzhi Zhang, Xuemei Wang, Yihang Zhou, Qiyu Jin, Guoqing Chen, Dong Liang, Haifeng Wang
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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.
Comments: Accepted as ISMRM 2024 Digital poster 6575. 04-09 May 2024 Singapore
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2408.11840 [cs.CV]
  (or arXiv:2408.11840v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2408.11840
Journal reference: ISMRM 2024 Digital poster 6575

Submission history

From: Taofeng Xie [view email]
[v1] Wed, 7 Aug 2024 04:01:50 UTC (3,044 KB)
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