364-01-001 ISMRM Abstract

Self-Regulating AI Agent for End-to-End Multi-model MRI Diagnostic Analysis of Neurodegenerative Diseases

Primary: Brain Function and fMRI - fMRI Analysis

Secondary: Acquisition & Reconstruction - AI methods

364-01-001 · fMRI: Analyses · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row E

Keywords: Autonomous MRI Reconstruction Interpretable Dynamic Graph Learning Neurodegenerative Disease Diagnosis

Accepted

Xingyang Wu ¹, Shuo Zhou¹, Isah S Ahmad^2,3, Fanshi li¹, Yanjie Zhu¹, Yihang Zhou⁴, Dong Liang⁴, Zhanqi hu³, Haifeng Wang¹

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

²Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

³Department of Pediatric, Shenzhen Guangming District People's Hospital, Shenzhen, China

⁴Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Presenting Author: Xingyang Wu

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References

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7. Li, F., Wang, Z., Guo, Y., Liu, C., Zhu, Y., Zhou, Y., Li, J., Liang, D., & Wang, H. (2023). Dynamic dual-graph fusion convolutional network for Alzheimer's disease diagnosis. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 675-679). IEEE.

8. Li, F., Wang, Z., Guo, Y., Liu, C., Zhu, Y., Zhou, Y., Li, J., Liang, D., & Wang, H. (2023). Developing a dynamic graph network for interpretable analysis of multi-modal MRI data in Parkinson's disease diagnosis. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-4). IEEE.

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Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-01-001