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• Clinical Article •
Susceptibility-weighted imaging joint deep learning to explore the potential association between superficial cerebral veins and deep white matter lesions
WANG Yajie  XIE Qi  WU Jun  HAN Pengpeng  TAN Zhilin  LIAO Yanhui 

Cite this article as: Wang YJ, Xie Q, Wu J, et al. Susceptibility-weighted imaging joint deep learning to explore the potential association between superficial cerebral veins and deep white matter lesions[J]. Chin J Magn Reson Imaging, 2022, 13(5): 17-22. DOI:10.12015/issn.1674-8034.2022.05.004.


[Abstract] Objective To explore the relationship between superficial cerebral veins' (SCV) morphological parameters and deep white matter lesions (DWML) through establishing a deep learning algorithm model based on susceptibility-weighted imaging (SWI).Materials and Methods Three hundred and sixty-four healthy volunteers were recruited. All subjects underwent a routine head scan and SWI, SWI images of 200 subjects were randomly selected to reconstruct the minimum intensity projection (MinIP) image. The deep learning algorithms were used to analyze the MinIP images to establish the automatic quantification model of SCV morphological features. One hundred and sixty-four subjects were used for analyzing the morphological features of SCV in the bilateral cerebral hemispheres including diameter, tortuosity index (TI),and so on by using the deep learning models. According to whether there are DWML, the 164 subjects were divided into two groups including the group of DWML comprised 53 subjects and the group of no-deep white matter lesions (N-DWML) comprised 111subjects. The Mann-Whitney U test was used to compare the quantitative indicators of SCV between the group of DWML and N-DWML, and a binary logistic regression model was established for significant difference indicators and DWML.Results The training set's average accuracy rate of the deep learning algorithms model for analysis of SCV morphological features was 98.19%, the validation set's average accuracy rate was 98.02%, and the test set's average accuracy rate was 98.03%. There were no differences in the distribution of age and years of education in the quantitative indicators of SCV in bilateral cerebral hemispheres (P＞0.05), but there were significant differences in the distribution of gender, which showed that the number of SCV in males was significantly more than that of females (Right: P=0.004, Left: P＜0.001). No significant difference was found in the diameter and number of SCV in bilateral cerebral hemispheres between DWML group and N-DWML group (P＞0.05). However, the TI of SCV in DWML group was significantly larger than N-DWML group (P＜0.001). The TI of SCV in right cerebral hemisphere was significantly correlated with the occurrence of DWML (regression coefficient=2.035, P=0.015).Conclusions There is a potential correlation between the morphological changes of the SCV and the DWML. The changes in TI of the SCV may affect the local hemodynamic changes and lead to the occurrence of DWML.
[Keywords] susceptibility-weighted imaging；deep learning；magnetic resonance imaging；superficial cerebral veins；white matter lesions

WANG Yajie1   XIE Qi1*   WU Jun2   HAN Pengpeng2   TAN Zhilin1   LIAO Yanhui1  

1 Department of Imaging, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China

2 Institute of Software Application Technology, Guangzhou 511458, China

Xie Q, E-mail: eyqixie@scut.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Guangzhou Science and Technology Plan Project (No. 201907010020).
Received  2021-12-27
Accepted  2022-04-12
DOI: 10.12015/issn.1674-8034.2022.05.004
Cite this article as: Wang YJ, Xie Q, Wu J, et al. Susceptibility-weighted imaging joint deep learning to explore the potential association between superficial cerebral veins and deep white matter lesions[J]. Chin J Magn Reson Imaging, 2022, 13(5): 17-22. DOI:10.12015/issn.1674-8034.2022.05.004.

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