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Automatic labeling of cerebral arteries in magnetic resonance angiography

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Abstract

Objectives

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.

Materials and 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.

Conclusion

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.

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Abbreviations

AAIM:

Automatic atlas-based artery identification method

CoW:

Circle of Willis

ICA:

Internal carotid artery

MCA:

Middle cerebral artery

ACA:

Anterior cerebral artery

VA:

Vertebral artery

BA:

Basilar artery

PCA:

Posterior cerebral artery

PCoA:

Posterior communicating artery

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Acknowledgments

This study was supported by the Swedish Research Council Grant 621-2011-5216, the Swedish Heart and Lung Foundation Grants 20110383 and 20140592 and the Swedish Brain Foundation. A grant from the Swedish Research Council (421-2012-648) to Lars Nyberg supported the COBRA data collection. The atlas will be made publicly available at an Umeå University website at time of publication.

Authors’ contribution

Dunås: Protocol/project development, Data collection or management, Data analysis. Wåhlin: Protocol/project development, Data collection or management, Data analysis. Ambarki: Protocol/project development, Data analysis. Zarrinkoob: Data analysis. Birgander: Data analysis. Malm: Protocol/project development, Data analysis. Eklund: Protocol/project development, Data analysis.

Author information

Authors and Affiliations

  1. Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden

    Tora Dunås, Anders Wåhlin, Khalid Ambarki, Richard Birgander & Anders Eklund

  2. Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden

    Anders Wåhlin & Anders Eklund

  3. Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden

    Khalid Ambarki & Anders Eklund

  4. Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden

    Laleh Zarrinkoob & Jan Malm

Authors
  1. Tora Dunås
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Corresponding author

Correspondence to Tora Dunås.

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Conflict of interest

The authors each declare that they have no conflict of interest.

Ethical standards

The local ethical review board approved the COBRA study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants in the study.

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Dunås, T., Wåhlin, A., Ambarki, K. et al. Automatic labeling of cerebral arteries in magnetic resonance angiography. Magn Reson Mater Phy 29, 39–47 (2016). https://doi.org/10.1007/s10334-015-0512-5

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  • DOI: https://doi.org/10.1007/s10334-015-0512-5

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