Generative AI for brain image computing and brain network computing: a review
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 n...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1203104/full |
| _version_ | 1797805796718804992 |
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| author | Changwei Gong Changwei Gong Changhong Jing Changhong Jing Xuhang Chen Xuhang Chen Chi Man Pun Guoli Huang Ashirbani Saha Martin Nieuwoudt Han-Xiong Li Yong Hu Shuqiang Wang Shuqiang Wang |
| author_facet | Changwei Gong Changwei Gong Changhong Jing Changhong Jing Xuhang Chen Xuhang Chen Chi Man Pun Guoli Huang Ashirbani Saha Martin Nieuwoudt Han-Xiong Li Yong Hu Shuqiang Wang Shuqiang Wang |
| author_sort | Changwei Gong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T05:57:30Z |
| format | Article |
| id | doaj.art-f5dd4fb232ec45a5b0642d07382a8bb2 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T05:57:30Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-f5dd4fb232ec45a5b0642d07382a8bb22023-06-13T04:17:09ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-06-011710.3389/fnins.2023.12031041203104Generative AI for brain image computing and brain network computing: a reviewChangwei Gong0Changwei Gong1Changhong Jing2Changhong Jing3Xuhang Chen4Xuhang Chen5Chi Man Pun6Guoli Huang7Ashirbani Saha8Martin Nieuwoudt9Han-Xiong Li10Yong Hu11Shuqiang Wang12Shuqiang Wang13Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Computer Science, University of Chinese Academy of Sciences, Beijing, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Computer Science, University of Chinese Academy of Sciences, Beijing, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Computer and Information Science, University of Macau, Macau, ChinaDepartment of Computer and Information Science, University of Macau, Macau, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Oncology and School of Biomedical Engineering, McMaster University, Hamilton, ON, CanadaInstitute for Biomedical Engineering, Stellenbosch University, Stellenbosch, South AfricaDepartment of Systems Engineering, City University of Hong Kong, Hong Kong, ChinaDepartment of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, ChinaDepartment of Computer Science, University of Chinese Academy of Sciences, Beijing, ChinaRecent 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1203104/fullgenerative modelsbrain imagingbrain networkdiffusion modelgenerative adversarial networkvariational autoencoder |
| spellingShingle | Changwei Gong Changwei Gong Changhong Jing Changhong Jing Xuhang Chen Xuhang Chen Chi Man Pun Guoli Huang Ashirbani Saha Martin Nieuwoudt Han-Xiong Li Yong Hu Shuqiang Wang Shuqiang Wang Generative AI for brain image computing and brain network computing: a review Frontiers in Neuroscience generative models brain imaging brain network diffusion model generative adversarial network variational autoencoder |
| title | Generative AI for brain image computing and brain network computing: a review |
| title_full | Generative AI for brain image computing and brain network computing: a review |
| title_fullStr | Generative AI for brain image computing and brain network computing: a review |
| title_full_unstemmed | Generative AI for brain image computing and brain network computing: a review |
| title_short | Generative AI for brain image computing and brain network computing: a review |
| title_sort | generative ai for brain image computing and brain network computing a review |
| topic | generative models brain imaging brain network diffusion model generative adversarial network variational autoencoder |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1203104/full |
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