Generative AI for brain image computing and brain network computing: a review

被引:50
作者
Gong, Changwei [1 ,2 ]
Jing, Changhong [1 ,2 ]
Chen, Xuhang [1 ,3 ]
Pun, Chi Man [3 ]
Huang, Guoli [1 ]
Saha, Ashirbani [4 ,5 ]
Nieuwoudt, Martin [6 ]
Li, Han-Xiong [7 ]
Hu, Yong [8 ]
Wang, Shuqiang [1 ,2 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[2] Univ Chinese Acad Sci, Dept Comp Sci, Beijing, Peoples R China
[3] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China
[4] McMaster Univ, Dept Oncol, Hamilton, ON, Canada
[5] McMaster Univ, Sch Biomed Engn, Hamilton, ON, Canada
[6] Stellenbosch Univ, Inst Biomed Engn, Stellenbosch, South Africa
[7] City Univ Hong Kong, Dept Syst Engn, Hong Kong, Peoples R China
[8] Univ Hong Kong, Dept Orthopaed & Traumatol, Hong Kong, Peoples R China
关键词
generative models; brain imaging; brain network; diffusion model; generative adversarial network; variational autoencoder; FUNCTIONAL CONNECTIVITY; AUTOENCODER; GAN;
D O I
10.3389/fnins.2023.1203104
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.
引用
收藏
页数:18
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