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A body examination study of brain MRI registration in adolescents using fusion generative adversarial network
ZHU Haiyan  LI Meng  JI Yuelong  ZHANG Fuchun  WANG Baiyang 

Cite this article as: ZHU H Y, LI M, JI Y L, et al. A body examination study of brain MRI registration in adolescents using fusion generative adversarial network[J]. Chin J Magn Reson Imaging, 2023, 14(2): 116-124. DOI:10.12015/issn.1674-8034.2023.02.020.

[Abstract] Objective To solve the problem of information loss in the sampling process of UNet framework, we used brain MRI of adolescents to study the problems of weak network learning ability and low accuracy of registration of brain marginal regions.Materials and Methods In this study, publicly available brain MRI data sets were used: HBN and LPBA40 propose a multiscale attention mechanisms generative adversarial networks (MAM_GAN). Single-mode brain image registration was realized. The method consists of registration network and authentication network. By adding multiscale attention mechanisms (MAM) modules to the identification network to acquire contextual information at different scales, more effective brain structural features were extracted during adversarial training. Secondly, the local cross-correlation loss function of image similarity was introduced into the registration network to constrain the similarity between the moving image and the fixed image, which further improves the image registration performance in the antagonistic training process of the two networks. Dice coefficient (Dice), structural similarity (SSIM) and Pearson's correlation coefficient (PCC) were used to measure the registration accuracy of registration image and fixed image.Results Compared with the traditional methods in Dice score, the accuracy of MAM_GAN method in cerebrospinal fluid (CSF), gray matter (gray matter, GM) and white matter (white matter, WM) increased by 0.013, 0.023 and 0.028 respectively, PCC score increased by 0.004 and SSIM score increased by 0.011. Hence, the experimental results showed that the method had good registration effect.Conclusions The MAM_GAN method can better learn the structural features of the brain, improve the registration performance, and provide a technical basis for the clinical diagnosis and physical detection of attention-deficit hyper-activity disorder (ADHD) in adolescents.
[Keywords] generative adversarial network;adolescent;attention-deficit hyper-activity disorder;imaging registration;multiscale;magnetic resonance image;attention mechanism;local cross-correlation

ZHU Haiyan1   LI Meng2*   JI Yuelong2   ZHANG Fuchun2   WANG Baiyang2  

1 School of Physical Education and Health, Linyi University, Linyi 276005, China

2 School of Computer Science and Engineering, Linyi University, Linyi 276005, China

*Correspondence to: Li M, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Shandong Social Science Planning and Research Project (No. 21CTYJ03).
Received  2022-08-09
Accepted  2022-12-12
DOI: 10.12015/issn.1674-8034.2023.02.020
Cite this article as: ZHU H Y, LI M, JI Y L, et al. A body examination study of brain MRI registration in adolescents using fusion generative adversarial network[J]. Chin J Magn Reson Imaging, 2023, 14(2): 116-124. DOI:10.12015/issn.1674-8034.2023.02.020.

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