title: A statistical cerebroarterial atlas derived from 700 MRA datasets.
abstract: OBJECTIVE
  The cerebroarterial system is a complex network of arteries that supply the brain cells with vitally important nutrients and oxygen. The inter-individual differences of the cerebral arteries, especially at a finer level, are still not understood sufficiently. The aim of this work is to present a statistical cerebroarterial atlas that can be used to overcome this problem.
  
  
  METHODS
  Overall, 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects were used for atlas generation. Therefore, the cerebral arteries were automatically segmented in each dataset and used for a quantification of the vessel diameters. After this, each TOF MRA dataset as well as the corresponding vessel segmentation and vessel diameter dataset were registered to the MNI brain atlas. Finally, the registered datasets were used to calculate a statistical cerebroarterial atlas that incorporates information about the average TOF intensity, probability for a vessel occurrence and mean vessel diameter for each voxel.
  
  
  RESULTS
  Visual analysis revealed that arteries with a diameter as small as 0.5 mm are well represented in the atlas with quantitative values that are within range of anatomical reference values. Moreover, a highly significant strong positive correlation between the vessel diameter and occurrence probability was found. Furthermore, it was shown that an intensity-based automatic segmentation of cerebral vessels can be considerable improved by incorporating the atlas information leading to results within the range of the inter-observer agreement.
  
  
  CONCLUSIONS
  The presented cerebroarterial atlas seems useful for improving the understanding about normal variations of cerebral arteries, initialization of cerebrovascular segmentation methods and may even lay the foundation for a reliable quantification of subtle morphological vascular changes.
authors.affiliation: Nils Daniel Forkert, Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Bldg. W36, Martinistraße 52, 20246 Hamburg, Germany, E-mail: n.forkert@uke.uni-hamburg.de.
authors.forename: N D
authors.initials: ND
authors.lastname: Forkert
authors.forename: J
authors.initials: J
authors.lastname: Fiehler
authors.forename: S
authors.initials: S
authors.lastname: Suniaga
authors.forename: H
authors.initials: H
authors.lastname: Wersching
authors.forename: S
authors.initials: S
authors.lastname: Knecht
authors.forename: A
authors.initials: A
authors.lastname: Kemmling
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D002533:Cerebral Angiography / Q000379:methods*; D002536:Cerebral Arteries / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011336:Probability; D012016:Reference Values
keywords: Magnetic resonance imaging; angiography; arteries; computer-assisted image processing; statistical atlas