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Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease
HUANG Jing  LUO Tianyou 

Cite this article as: HUANG J, LUO T Y. Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease[J]. Chin J Magn Reson Imaging, 2024, 15(2): 172-177. DOI:10.12015/issn.1674-8034.2024.02.027.


[Abstract] With the aging of the population, the prevalence of age-related cerebral small vessel disease (CSVD) is on the rise. CSVD frequently results in cognitive impairment and dementia, making it a pressing public health concern. Nevertheless, the pathogenesis of cognitive impairment related to CSVD has not yet been fully elucidated, and there is a lack of effective methods for early diagnosis and treatment. With the rapid development of neuroimaging technology and artificial intelligence, multiparametric MRI and machine learning are playing an increasingly important role in the auxiliary diagnosis and pathogenesis exploration of cognitive impairment related to CSVD. This article provides a review of the relevant research progress in recent years, aiming to provide comprehensive and objective imaging evidence for elucidating the neural mechanisms and early diagnosis of cognitive impairment related to CSVD.
[Keywords] cerebral small vessel disease；cognitive impairment；magnetic resonance imaging；multiparametric magnetic resonance imaging；machine learning

HUANG Jing   LUO Tianyou*  

Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

Corresponding author: LUO T Y, E-mail: ltychy@sina.com

Conflicts of interest   None.

Received  2023-11-07
Accepted  2024-02-02
DOI: 10.12015/issn.1674-8034.2024.02.027
Cite this article as: HUANG J, LUO T Y. Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease[J]. Chin J Magn Reson Imaging, 2024, 15(2): 172-177. DOI:10.12015/issn.1674-8034.2024.02.027.

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