Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Taofeng Xie; Chentao Cao; Zhuoxu Cui; Yu Guo; Caiying Wu; Xuemei Wang; Qingneng Li; Zhanli Hu; Tao Sun; Ziru Sang; Yihang Zhou; Yanjie Zhu; Dong Liang; Qiyu Jin; Hongwu Zeng; Guoqing Chen; Haifeng Wang

arXiv · 2305.03901

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

Taofeng Xie·Chentao Cao·Zhuoxu Cui·Yu Guo·Caiying Wu·Xuemei Wang·Qingneng Li·Zhanli Hu·Tao Sun·Ziru Sang·Yihang Zhou·Yanjie Zhu·Dong Liang·Qiyu Jin·Hongwu Zeng·Guoqing Chen·Haifeng Wang

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Abstract

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.

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Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhou, Yanjie Zhu, Dong Liang, Qiyu Jin, Hongwu Zeng, Guoqing Chen, Haifeng Wang. 2024-06-19. Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model. https://arxiv.org/abs/2305.03901

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