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

Frontiers in Neuroscience

Neuroscience

Chi Man Pun¹  Ashirbani Saha²  Yong Hu³  Han-Xiong Li⁴  Martin Nieuwoudt⁵  Guoli Huang⁶  Shuqiang Wang⁷  Changwei Gong⁷  Changhong Jing⁷  Xuhang Chen⁸ 

[1] Department of Computer and Information Science, University of Macau, Macau, China;Department of Oncology and School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada;Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China;Department of Systems Engineering, City University of Hong Kong, Hong Kong, China;Institute for Biomedical Engineering, Stellenbosch University, Stellenbosch, South Africa;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Department of Computer Science, University of Chinese Academy of Sciences, Beijing, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Department of Computer and Information Science, University of Macau, Macau, China;

关键词: generative models;    brain imaging;    brain network;    diffusion model;    generative adversarial network;    variational autoencoder;   

DOI  :  10.3389/fnins.2023.1203104

 received in 2023-04-10, accepted in 2023-05-22,  发布年份 2023

来源: Frontiers

【 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.

【 授权许可】

Unknown   
Copyright © 2023 Gong, Jing, Chen, Pun, Huang, Saha, Nieuwoudt, Li, Hu and Wang.