title: The feasibility of quantitative MRI of perivascular spaces at 7T.
abstract: BACKGROUND
  Dilated brain perivascular spaces (PVSs) are found to be associated with many conditions, including aging, dementia, and Alzheimer's disease (AD). Conventionally, PVS assessment is mainly based on subjective observations of the number, size and shape of PVSs in MR images collected at clinical field strengths (≤3T). This study tests the feasibility of imaging and quantifying brain PVS with an ultra-high 7T whole-body MRI scanner.
  
  
  METHODS
  3D high resolution T2-weighted brain images from healthy subjects (n=3) and AD patients (n=5) were acquired on a 7T whole-body MRI scanner. To automatically segment the small hyperintensive fluid-filling PVS structures, we also developed a quantitative program based on algorithms for spatial gradient, component connectivity, edge-detection, k-means clustering, etc., producing quantitative results of white matter PVS volume densities.
  
  
  RESULTS
  The 3D maps of automatically segmented PVS show an apparent increase in PVS density in AD patients compared to age-matched healthy controls due to the PVS dilation (8.0±2.1 v/v% in AD vs. 4.9±1.3 v/v% in controls, p<0.05).
  
  
  CONCLUSIONS
  We demonstrated that 7T provides sufficient SNR and resolution for quantitatively measuring PVSs in deep white matter that is challenging with clinical MRI systems (≤3T). Compared to the conventional visual counting and rating for the PVS assessment, the quantitation method we developed is automatic and objective.
  
  
  CONCLUSIONS
  Quantitative PVS MRI at 7T may serve as a non-invasive and endogenous imaging biomarker for diseases with PVS dilation.
authors.affiliation: Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA; Center for MR Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA. Electronic address: kcai@uic.edu.
authors.forename: Kejia
authors.initials: K
authors.lastname: Cai
authors.affiliation: Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA; Center for MR Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Rongwen
authors.initials: R
authors.lastname: Tain
authors.affiliation: Department of Radiology, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Sandhitsu
authors.initials: S
authors.lastname: Das
authors.affiliation: Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA; Center for MR Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Frederick C
authors.initials: FC
authors.lastname: Damen
authors.affiliation: Center for MR Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Sui
authors.affiliation: Department of Neuropathology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Tibor
authors.initials: T
authors.lastname: Valyi-Nagy
authors.affiliation: Department of Radiology, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Elliott
authors.affiliation: Department of Radiology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA; Center for MR Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
authors.forename: Xiaohong J
authors.initials: XJ
authors.lastname: Zhou
mesh_terms: D000368:Aged; D000375:Aging / Q000473:pathology; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology*; D005240:Feasibility Studies; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D010363:Pattern Recognition, Automated; D051598:Whole Body Imaging / Q000295:instrumentation
keywords: Alzheimer's disease; MRI; Perivascular drainage; Perivascular space