Automatic quantification of perivascular spaces in T2-weighted images at 7 T MRI

• Published in: Cerebral circulation - cognition and behavior 2022-01, Vol.3, p.100142, Article 100142
• Main Authors: Spijkerman, J.M., Zwanenburg, J.J.M., Bouvy, W.H., Geerlings, M.I., Biessels, G.J., Hendrikse, J., Luijten, P.R., Kuijf, H.J.
• Format: Article
• Language: English
• Subjects: 7 tesla MRI; Centrum semiovale; Machine learning; Perivascular spaces; Quantification
• ISSN: 2666-2450; 2666-2450
• DOI: 10.1016/j.cccb.2022.100142

•Fully automated detection of perivascular spaces on 7T MRI.•Good correlation with manual assessments of perivascular spaces.•Quantitative measurements of PVS characteristics: density, length, and tortuosity. Perivascular spaces (PVS) are believed to be involved in brain waste disposal. PVS are associated with cerebral small vessel disease. At higher field strengths more PVS can be observed, challenging manual assessment. We developed a method to automatically detect and quantify PVS. A machine learning approach identified PVS in an automatically positioned ROI in the centrum semiovale (CSO), based on -resolution T2-weighted TSE scans. Next, 3D PVS tracking was performed in 50 subjects (mean age 62.9 years (range 27–78), 19 male), and quantitative measures were extracted. Maps of PVS density, length, and tortuosity were created. Manual PVS annotations were available to train and validate the automatic method. Good correlation was found between the automatic and manual PVS count: ICC (absolute/consistency) is 0.64/0.75, and Dice similarity coefficient (DSC) is 0.61. The automatic method counts fewer PVS than the manual count, because it ignores the smallest PVS (length