扩散张量成像联合人工智能在脑小血管病中的应用进展OA

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.