A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-01

AUTHORS

Tora Dunås, Anders Wåhlin, Khalid Ambarki, Laleh Zarrinkoob, Jan Malm, Anders Eklund

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. More... »

PAGES

101-110

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12021-016-9320-y

DOI

http://dx.doi.org/10.1007/s12021-016-9320-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002411168

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27873151


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12021-016-9320-y'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12021-016-9320-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12021-016-9320-y'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12021-016-9320-y'


 

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