Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911951505850368 |
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| author | Xie, Taofeng Cui, Zhuo-Xu Luo, Chen Wang, Huayu Liu, Congcong Zhang, Yuanzhi Wang, Xuemei Zhu, Yanjie Chen, Guoqing Liang, Dong Jin, Qiyu Zhou, Yihang Wang, Haifeng |
| author_facet | Xie, Taofeng Cui, Zhuo-Xu Luo, Chen Wang, Huayu Liu, Congcong Zhang, Yuanzhi Wang, Xuemei Zhu, Yanjie Chen, Guoqing Liang, Dong Jin, Qiyu Zhou, Yihang Wang, Haifeng |
| contents | Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. However, there exists complementary information among multi-modal images. The complementary information can contribute to image reconstruction. In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion. MC-Diffusion learns the joint probability distribution of PET and MRI for utilizing complementary information. We conducted a series of contrast experiments about LPLS, Joint ISAT-net and MC-Diffusion by the ADNI dataset. The results underscore the qualitative and quantitative improvements achieved by MC-Diffusion, surpassing the state-of-the-art method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14473 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction Xie, Taofeng Cui, Zhuo-Xu Luo, Chen Wang, Huayu Liu, Congcong Zhang, Yuanzhi Wang, Xuemei Zhu, Yanjie Chen, Guoqing Liang, Dong Jin, Qiyu Zhou, Yihang Wang, Haifeng Image and Video Processing Computer Vision and Pattern Recognition Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. However, there exists complementary information among multi-modal images. The complementary information can contribute to image reconstruction. In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion. MC-Diffusion learns the joint probability distribution of PET and MRI for utilizing complementary information. We conducted a series of contrast experiments about LPLS, Joint ISAT-net and MC-Diffusion by the ADNI dataset. The results underscore the qualitative and quantitative improvements achieved by MC-Diffusion, surpassing the state-of-the-art method. |
| title | Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.14473 |