title: Automatic labeling of cerebral arteries in magnetic resonance angiography.
abstract: OBJECTIVE
  In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram.
  
  
  METHODS
  Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals.
  
  
  RESULTS
  Overall accuracy was 93%, and internal carotid artery and middle cerebral artery labeling was 100% accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89%, respectively.
  
  
  CONCLUSIONS
  The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden. tora.dunas@umu.se.
authors.forename: Tora
authors.initials: T
authors.lastname: Dunås
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.|Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Wåhlin
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.|Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Khalid
authors.initials: K
authors.lastname: Ambarki
authors.affiliation: Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Laleh
authors.initials: L
authors.lastname: Zarrinkoob
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Richard
authors.initials: R
authors.lastname: Birgander
authors.affiliation: Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Jan
authors.initials: J
authors.lastname: Malm
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.|Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden.|Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Eklund
mesh_terms: D000368:Aged; D000792:Angiography / Q000379:methods; D001331:Automation; D002343:Carotid Artery, Internal / Q000000981:diagnostic imaging; D002536:Cerebral Arteries / Q000000981:diagnostic imaging*; D002560:Cerebrovascular Circulation; D003097:Collateral Circulation; D003952:Diagnostic Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D011336:Probability; D015203:Reproducibility of Results; D014711:Vertebral Artery / Q000000981:diagnostic imaging
keywords: Atlases as topic; Automatic data processing; Cerebral angiography; Circle of Willis; Magnetic resonance angiography