Validation of T1w‐based segmentations of white matter hyperintensity volumes in large‐scale datasets of aging

• Published in: Human brain mapping Vol. 39; no. 3; pp. 1093 - 1107
• Main Authors: Dadar, Mahsa, Maranzano, Josefina, Ducharme, Simon, Carmichael, Owen T., Decarli, Charles, Collins, D. Louis
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2018; John Wiley and Sons Inc
• Subjects: Age; Aged; Aging; Aging - pathology; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Azheimer's disease; Brain - diagnostic imaging; Brain - pathology; cerebrovascular disease; Cognitive ability; cognitive impairment; Datasets; Datasets as Topic; Economic conditions; Female; Humans; Image Processing, Computer-Assisted - methods; Lobes; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Organ Size; Pattern Recognition, Automated; Proton density (concentration); small vessel disease; Substantia alba; T1w hypointensities; White Matter - diagnostic imaging; White Matter - pathology; white matter integrity; white matter integrity; small vessel disease; cognitive impairment; cerebrovascular disease; Azheimer's disease; T1w hypointensities
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23894

Introduction Fluid‐attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints. Methods In this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information. Results The WMHLs obtained from T1w‐Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p < .001, ADNI2/GO: r = .904, p < .001), segmentations obtained from T1w + T2w + PD for ADNI1 (r = .888, p < .001) and T1w + FLAIR for ADNI2/GO (r = .969, p < .001), age (ADNI1: r = .391, p < .001, ADNI2/GO: r = .466, p < .001), and ADAS13 (ADNI1: r = .227, p < .001, ADNI2/GO: r = .190, p < 0.001), and NPI (ADNI1: r = .290, p < .001, ADNI2/GO: r = 0.144, p < .001), controlling for age. Conclusion Our results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w‐Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.