Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging

Daniel L. Schwartz, Erin Boespflug, David L. Lahna, Jeffrey Pollock, Natalie E. Roese, Lisa Silbert

Research output: Contribution to journalArticle

Abstract

Recent interest in enlarged perivascular spaces (ePVS) in the brain, which can be visualized on MRI and appear isointense to cerebrospinal fluid on all sequence weightings, has resulted in the necessity of reliable algorithms for automated segmentation to allow for whole brain assessment of ePVS burden. However, several publicly available datasets do not contain sequences required for recently published algorithms. This prospective study presents a method for identification of enlarged perivascular spaces (ePVS) in white matter using 3T T1 and FLAIR MR imaging (MAPS-T1), making the algorithm accessible to groups with valuable sets of limited data. The approach was applied identically to two datasets: 1) a repeated measurement in a dementia-free aged human population (N = 14), and 2) an aged sample of multisite ADNI datasets (N = 30). ePVS segmentation was accomplished by a stepwise local homogeneity search of white matter-masked T1-weighted data, constrained by FLAIR hyperintensity, and further constrained by width, volume, and linearity measurements. Pearson's r was employed for statistical testing between visual (gold standard) assessment and repeated measures in cohort one. Visual ePVS counts were significantly correlated with MAPS-T1 (r = .72, P < .0001). Correlations between repeated measurements in cohort one were significant for both visual and automated methods in the single visually-rated slice (MAPS-T1: r = .87, P < .0001, visual: (r = .86, P < .0001) and for whole brain assessment (MAPS-T1: r = .77, P = .001). Results from each cohort were manually inspected and found to have positive predictive values of 77.5% and 87.5%, respectively. The approach described in this report is an important tool for detailed assessment of ePVS burden in white matter on routinely acquired MRI sequences.

Original languageEnglish (US)
Article number116126
JournalNeuroImage
Volume202
DOIs
StatePublished - Nov 15 2019

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Brain
Cerebrospinal Fluid
Dementia
Prospective Studies
White Matter
Datasets
Population

Keywords

  • Alzheimer's
  • Enlarged perivascular space
  • MRI
  • Segmentation
  • Vascular disease
  • Virchow-robin

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Autoidentification of perivascular spaces in white matter using clinical field strength T1 and FLAIR MR imaging. / Schwartz, Daniel L.; Boespflug, Erin; Lahna, David L.; Pollock, Jeffrey; Roese, Natalie E.; Silbert, Lisa.

In: NeuroImage, Vol. 202, 116126, 15.11.2019.

Research output: Contribution to journalArticle

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