Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914920485879808 |
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| author | Xie, Taofeng Cui, Zhuoxu Liu, Congcong Luo, Chen Wang, Huayu Zhang, Yuanzhi Wang, Xuemei Zhou, Yihang Jin, Qiyu Chen, Guoqing Liang, Dong Wang, Haifeng |
| author_facet | Xie, Taofeng Cui, Zhuoxu Liu, Congcong Luo, Chen Wang, Huayu Zhang, Yuanzhi Wang, Xuemei Zhou, Yihang Jin, Qiyu Chen, Guoqing Liang, Dong Wang, Haifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11840 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model Xie, Taofeng Cui, Zhuoxu Liu, Congcong Luo, Chen Wang, Huayu Zhang, Yuanzhi Wang, Xuemei Zhou, Yihang Jin, Qiyu Chen, Guoqing Liang, Dong Wang, Haifeng Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2408.11840 |