title: Development and initial evaluation of a semi-automatic approach to assess perivascular spaces on conventional magnetic resonance images.
abstract: OBJECTIVE
  Perivascular spaces (PVS) are associated with ageing, cerebral small vessel disease, inflammation and increased blood brain barrier permeability. Most studies to date use visual rating scales to assess PVS, but these are prone to observer variation.
  
  
  METHODS
  We developed a semi-automatic computational method that extracts PVS on bilateral ovoid basal ganglia (BG) regions on intensity-normalised T2-weighted magnetic resonance images. It uses Analyze™10.0 and was applied to 100 mild stroke patients' datasets. We used linear regression to test association between BGPVS count, volume and visual rating scores; and between BGPVS count & volume, white matter hyperintensity (WMH) rating scores (periventricular: PVH; deep: DWMH) & volume, atrophy rating scores and brain volume.
  
  
  RESULTS
  In the 100 patients WMH ranged from 0.4 to 119ml, and total brain tissue volume from 0.65 to 1.45l. BGPVS volume increased with BGPVS count (67.27, 95%CI [57.93 to 76.60], p<0.001). BGPVS count was positively associated with WMH visual rating (PVH: 2.20, 95%CI [1.22 to 3.18], p<0.001; DWMH: 1.92, 95%CI [0.99 to 2.85], p<0.001), WMH volume (0.065, 95%CI [0.034 to 0.096], p<0.001), and whole brain atrophy visual rating (1.01, 95%CI [0.49 to 1.53], p<0.001). BGPVS count increased as brain volume (as % of ICV) decreased (-0.33, 95%CI [-0.53 to -0.13], p=0.002).
  
  
  CONCLUSIONS
  BGPVS count and volume increased with the overall increase of BGPVS visual scores (2.11, 95%CI [1.36 to 2.86] for count and 0.022, 95%CI [0.012 to 0.031] for volume, p<0.001). Distributions for PVS count and visual scores were also similar.
  
  
  CONCLUSIONS
  This semi-automatic method is applicable to clinical protocols and offers quantitative surrogates for PVS load. It shows good agreement with a visual rating scale and confirmed that BGPVS are associated with WMH and atrophy measurements.
authors.affiliation: Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK. Electronic address: M.Valdes-Hernan@ed.ac.uk.
authors.forename: Maria Del C
authors.initials: Mdel C
authors.lastname: Valdés Hernández
authors.affiliation: Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
authors.forename: Fergus
authors.initials: F
authors.lastname: Doubal
authors.affiliation: Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
authors.forename: Francesca M
authors.initials: FM
authors.lastname: Chappell
authors.affiliation: College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
authors.forename: Rory J
authors.initials: RJ
authors.lastname: Piper
authors.affiliation: Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK.
authors.forename: Ian J
authors.initials: IJ
authors.lastname: Deary
authors.affiliation: Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK.
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Wardlaw
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001284:Atrophy; D001921:Brain / Q000098:blood supply* / Q000473:pathology*; D015331:Cohort Studies; D066264:Datasets as Topic; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D012720:Severity of Illness Index; D020521:Stroke / Q000453:epidemiology / Q000473:pathology; D006113:United Kingdom / Q000453:epidemiology; D066127:White Matter / Q000098:blood supply / Q000473:pathology
keywords: MRI; Perivascular spaces; Small vessel disease; Virchow–Robin spaces