Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

• Published in: Chinese medical journal Vol. 134; no. 2; pp. 151 - 160
• Main Authors: Zhao, Lei, Lee, Allan, Fan, Yu-Hua, Mok, Vincent C.T., Shi, Lin
• Format: Journal Article
• Language: English
• Published: China Lippincott Williams & Wilkins 16.12.2020; BrainNow Research Institute, Shenzhen, Guangdong 518000, China%Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou, Guangdong 510080, China%Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong 999077, China%Gerald Choa Neuroscience Centre, Margaret KL Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China%Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong 999077, China; Wolters Kluwer
• Subjects: Review; Automated quantification; Clinical relevance; Cerebral small vessel disease; Neuroimaging manifestations
• ISSN: 0366-6999; 2542-5641; 2542-5641
• DOI: 10.1097/CM9.0000000000001299

The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.