title: A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries.
abstract: Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling.
authors.affiliation: Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden. tora.dunas@umu.se.
authors.forename: Tora
authors.identifier: 0000-0002-5911-9511
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 Pharmacology and Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden.
authors.forename: Laleh
authors.initials: L
authors.lastname: Zarrinkoob
authors.affiliation: Department of Pharmacology and 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; D001271:Atlases as Topic; D002536:Cerebral Arteries / Q000166:cytology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods
keywords: 4D flow MRI; Automatic labeling; Cerebral arteries; Probabilistic atlas; Spatial normalization