Structural Brain Network Generation via Brain Denoising Diffusion Probabilistic Model

Jiang, Hongjie1,2; Chen, Xuhang2; Jin, Changhong2; Wang, Shuqiang2

10.1007/978-3-031-67278-1_21

2024

1st International Conference on Artificial Intelligence in Healthcare (AIiH)

0302-9743

1611-3349

978-3-031-67277-4

ARTIFICIAL INTELLIGENCE IN HEALTHCARE, PT I, AIIH 2024

14975

SEP 04-06, 2024

null,Swansea,ENGLAND

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

Alzheimer's disease (AD) significantly impairs the quality of life for a vast patient population and poses treatment challenges, partly due to its elusive pathophysiological mechanisms. The analysis of structural brain networks is fundamental to elucidating these mechanisms. However, acquiring data on such networks is non-trivial, hindered by the slow generation of structural brain network data and the complexities involved in obtaining DTI, a key requisite for their construction. In this study, we introduce a brain denoising diffusion probabilistic model designed to synthesize structural brain networks at various stages of AD, thereby mitigating the difficulties inherent in data acquisition. We trained this model on the ADNI dataset, utilizing two sets of data: one comprising structural brain networks produced by PANDA, and the other amalgamating these PANDA-generated networks with those synthesized by our model. Both datasets were employed to train a DiffPool for subsequent diagnostic tasks. It can be seen that the brain networks generated by the brain denoising diffusion probabilistic model are beneficial for structural brain networks in downstream diagnostic tasks.

Diffusion model strutural brain network graph neural network

第一

英语

CPCI-S

National Natural Science Foundations of China[62172403] ; Distinguished Young Scholars Fund of Guangdong[2021B1515020019]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Biomedical

WOS:001308380900021

Web of Science

1.South Univ Sci & Technol China, Shenzhen, Peoples R China
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China

Jiang, Hongjie,Chen, Xuhang,Jin, Changhong,et al. Structural Brain Network Generation via Brain Denoising Diffusion Probabilistic Model[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2024.