Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Motivation: 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. Goal(s): We aim to accelerate MRI and improve PET image quality. Approach: This paper proposed a novel joint reconstruction model by diffusion stochastic differential equations based on learning the joint probability distribution of PET and MRI. Results: 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. Impact: 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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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/4250
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Yihang Zhou, Zhuo‐Xu Cui, Hairong Zheng, Dong Liang et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Medical Imaging Techniques and Applications
article

Joint PET-MRI Reconstruction with Diffusion Stochastic Differential Model

Yihang Zhou, Zhuo‐Xu Cui, Hairong Zheng, Dong Liang, Chen Luo, Qiyu Jin, Yuanzhi Zhang, Huayu Wang, Guoqing Chen, Haifeng Wang, Xuemei Wang, Congcong Liu, Taofeng Xie
article en

Abstract

Motivation: 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. Goal(s): We aim to accelerate MRI and improve PET image quality. Approach: This paper proposed a novel joint reconstruction model by diffusion stochastic differential equations based on learning the joint probability distribution of PET and MRI. Results: 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. Impact: 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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Medical Imaging Techniques and Applications
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