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Advances in the application of diffusion tensor imaging combined with artificial intelligence in cerebral small vessel disease
ZHANG Chengwei  CUI Yong 

DOI:10.12015/issn.1674-8034.2026.01.025.


[Abstract] Cerebral small vessel disease (CSVD) is one of the most important causes of vascular cognitive impairment and recurrent stroke. It has an insidious onset, and conventional MRI often appears normal in the early stages, making it difficult to detect occult white matter microstructural damage, which frequently leads to delayed optimal intervention. Diffusion tensor imaging (DTI), through its core parameters such as fractional anisotropy, mean diffusivity, and derived metrics (e.g., diffusion tensor imaging analysis along perivascular spaces, peak width of skeletonized mean diffusivity, free water, etc.), can sensitively detect demyelination, microscopic edema, and glymphatic system dysfunction even when conventional MRI sequences show no obvious abnormalities. It has become the most important and sensitive noninvasive technique for assessing CSVD. This systematic review summarizes the imaging principles of DTI, as well as the latest applications of its core and derived parameters in the full spectrum of CSVD. It emphasizes the clinical value of DTI in early diagnosis, pathological mechanism elucidation, subtype differentiation, cognitive impairment prediction, and prognosis assessment. The limitations of current research are identified, and future research directions are proposed by integrating the research trends of artificial intelligence (AI) and multimodal image fusion. The aim is to provide clinicians and radiologists with a comprehensive understanding of the role of DTI combined with AI in CSVD evaluation and to offer insights for subsequent research.
[Keywords] cerebral small vessel disease；white matter microstructure；artificial intelligence；multi-modal imaging；magnetic resonance imaging；diffusion tensor imaging

ZHANG Chengwei   CUI Yong*  

Department of Radiology, Affiliated Hospital of Yanbian University, Yanbian 133000, China

Corresponding author: CUI Y, E-mail: 13019180807@163.com

Conflicts of interest   None.

Received  2025-09-29
Accepted  2025-12-12
DOI: 10.12015/issn.1674-8034.2026.01.025
DOI:10.12015/issn.1674-8034.2026.01.025.

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