Methods Inf Med 2013; 52(06): 467-474
DOI: 10.3414/ME13-02-0001
Original Articles
Schattauer GmbH

A Statistical Cerebroarterial Atlas Derived from 700 MRA Datasets

N. D. Forkert
1   Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
J. Fiehler
1   Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
S. Suniaga
2   Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
,
H. Wersching
3   Department of Epidemiology and Social Medicine, University of Münster, Münster, Germany
,
S. Knecht
4   Department of Neurology, University of Münster, Münster, Germany
,
A. Kemmling
1   Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
5   Department of Clinical Radiology, University of Münster, Münster, Germany
› Author Affiliations
Further Information

Publication History

received: 01 February 2013

accepted: 30 April 2013

Publication Date:
20 January 2018 (online)

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Summary

Objectives: The cerebroarterial system is a complex network of arteries that supply the brain cells with vitally important nutrients and oxygen. The inter-individual differences of the cerebral arteries, especially at a finer level, are still not understood sufficiently. The aim of this work is to present a statistical cerebroarterial atlas that can be used to overcome this problem.

Methods: Overall, 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) data sets of healthy subjects were used for atlas generation. Therefore, the cerebral arteries were automatically segmented in each dataset and used for a quantification of the vessel diameters. After this, each TOF MRA dataset as well as the corresponding vessel segmentation and vessel diameter dataset were registered to the MNI brain atlas. Fi -nally, the registered datasets were used to calculate a statistical cerebroarterial atlas that incorporates information about the average TOF intensity, probability for a vessel occurrence and mean vessel diameter for each voxel.

Results: Visual analysis revealed that arteries with a diameter as small as 0.5 mm are well represented in the atlas with quantitative values that are within range of anatomical reference values. Moreover, a highly significant strong positive correlation between the vessel diameter and occurrence probability was found. Furthermore, it was shown that an intensity-based automatic segmentation of cerebral vessels can be considerable improved by incorporating the atlas information leading to results within the range of the inter-observer agreement.

Conclusion: The presented cerebroarterial atlas seems useful for improving the understanding about normal variations of cerebral arteries, initialization of cerebrovascular segmentation methods and may even lay the foundation for a reliable quantification of subtle morphological vascular changes.