Download PDF
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)

    Permissions and Reprints

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.

Keywords

Magnetic resonance imaging - angiography - arteries - statistical atlas - computer-assisted image processing
 

  • References

  • 1 Bullitt E, Zeng D, Mortamet B, Ghosh A, Aylward SR, Lin W. et al. The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography. Neurobiol Aging 2010; 31 (02) 290-300.
    CrossrefPubMedGoogle Scholar
  • 2 Spangler KM, Challa VR, Moody DM, Bell MA. Arteriolar tortuosity of the white matter in aging and hypertension. A microradiographic study. J Neuropathol Exp Neurol 1994; 53 (01) 22-26.
    CrossrefPubMedGoogle Scholar
  • 3 Hashimura T, Kimura T, Miyakawa T. Morphological changes of blood vessels in the brain with Alzheimer’s disease. Jpn J Psychiatry Neurol 1991; 45 (03) 661-665.
    PubMedGoogle Scholar
  • 4 Nowinski WL, Volkau I, Marchenko Y, Thirunavuukarasuu A, Ng TT, Runge VM. A 3D model of human cerebrovasculature derived from 3T magnetic resonance angiography. Neuroinformatics 2009; 7 (01) 23-36.
    CrossrefPubMedGoogle Scholar
  • 5 Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K. et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001; 356 1412 1293-1322.
    CrossrefPubMedGoogle Scholar
  • 6 Cool D, Chillet D, Kim J, Guyon J-P. Foskey M, Aylward S. Tissue-Based Affine Registration of Brain Images to form a Vascular Density Atlas. In: Ellis R, Peters T. editors. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. Berlin, Heidelberg: Springer; 2003: 9-15.
    Google Scholar
  • 7 Dufour A, Tankyevych O, Talbot H, Ronse C, Baruthio J, Passat N. A statistical arteriovenous cerebral atlas. MICCAI Workshop on Computing and Visualization for (Intra)Vascular Imaging 2011: 73-80.
    Google Scholar
  • 8 Knecht S, Wersching H, Lohmann H, Bruchmann M, Duning T, Dziewas R. et al. High-normal blood pressure is associated with poor cognitive performance. Hypertension 2008; 51 (03) 663-668.
    CrossrefPubMedGoogle Scholar
  • 9 Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998; 17 (01) 87-97.
    CrossrefPubMedGoogle Scholar
  • 10 Forkert ND, Säring D, Fiehler J, Illies T, Möller D, Handels H. Automatic brain segmentation in Time-of-Flight MRA images. Methods Inf Med 2009; 48 (05) 399-407.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 11 Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T. et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998; 2 (02) 143-168.
    CrossrefPubMedGoogle Scholar
  • 12 Forkert ND, Schmidt-Richberg A, Fiehler J, Illies T, Moller D, Handels H. et al. Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation. Methods Inf Med 2011; 50 (01) 74-83.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Forkert ND, Schmidt-Richberg A, Fiehler J, Illies T, Moller D, Saring D. et al. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imaging 2013; 31 (02) 262-271.
    CrossrefPubMedGoogle Scholar
  • 14 Forkert ND, Schmidt-Richberg A, Fiehler J, Illies T, Moller D, Handels H. et al. Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets. Methods Inf Med 2012; 51 (05) 415-422.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Nyström I. Smedby Ö editors. New presentation method for magnetic resonance angiography images based on skeletonization. SPIE Medical Imaging: Image Display and Visualization; 2000.
    Google Scholar
  • 16 Wells WM, Viola P, Atsumi H, Nakajima S, Kikinis R. 3rd Multi-modal volume registration by maximization of mutual information. Med Image Anal 1996; 1 (01) 35-51.
    CrossrefPubMedGoogle Scholar
  • 17 Rhoton A. Rhoton’s Cranial Anatomy and Surgical Approaches: Lippincott Raven 2008.
  • 18 Grand W, Hopkins LN. Vasculature of the Brain and Cranial Base. Variations in Clinical Anatomy: Thieme; 1998.
    Google Scholar
  • 19 Harnsberger HR, Osborn AG, Ross J, Macdonald A. Diagnostic and Surgical Imaging Anatomy. Brain, Head and Neck, Spine.: Lippincott Williams & Wilkins; 2006.
    Google Scholar
  • 20 Kretschmann H-J. Weinrich W. Cranial Neuroimaging and Clinical Neuroanatomy. Magnetic Resonance Imaging and Computed Tomography: Thieme; 2003.
    Google Scholar
  • 21 Pena-Tapia PG, Kemmling A, Czabanka M, Vajkoczy P, Schmiedek P. Identification of the optimal cortical target point for extracranial-intracranial bypass surgery in patients with hemodynamic cerebrovascular insufficiency. J Neurosurg 2008; 108 (04) 655-661.
    CrossrefPubMedGoogle Scholar
  • 22 Persu A, De Plaen JF. Recent insights in the development of organ damage caused by hypertension. Acta Cardiol 2004; 59 (04) 369-381.
    CrossrefPubMedGoogle Scholar
  • 23 Mancia G, De Backer G, Dominiczak A, Cifkova R, Fagard R, Germano G. et al. 2007 Guidelines for the Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). J Hypertens 2007; 25 (06) 1105-1187.
    CrossrefPubMedGoogle Scholar
  • 24 John U, Greiner B, Hensel E, Ludemann J, Piek M, Sauer S. et al. Study of Health in Pomerania (SHIP): a health examination survey in an east German region: objectives and design. Soz Praeventivmed 2001; 46 (03) 186-194.
    CrossrefPubMedGoogle Scholar