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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
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2017 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 15, no 1, 101-110 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 15, no 1, 101-110 p.
Keyword [en]
Cerebral arteries, Probabilistic atlas, 4D flow MRI, Automatic labeling, Spatial normalization
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:umu:diva-131144DOI: 10.1007/s12021-016-9320-yISI: 000394260000009PubMedID: 27873151Scopus ID: 2-s2.0-84996542654OAI: oai:DiVA.org:umu-131144DiVA: diva2:1071866
Available from: 2017-02-06 Created: 2017-02-06 Last updated: 2017-04-04Bibliographically approved

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Dunås, ToraWåhlin, AndersAmbarki, KhalidZarrinkoob, LalehMalm, JanEklund, Anders
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Department of Radiation SciencesUmeå Centre for Functional Brain Imaging (UFBI)Centre for Biomedical Engineering and Physics (CMTF)Clinical Neuroscience
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CiteExportLink to record
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Citation style
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