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Fractal dimension and vessel complexity in patients with cerebral arteriovenous malformations.

Abstract
The fractal dimension (FD) can be used as a measure for morphological complexity in biological systems. The aim of this study was to test the usefulness of this quantitative parameter in the context of cerebral vascular complexity. Fractal analysis was applied on ten patients with cerebral arteriovenous malformations (AVM) and ten healthy controls. Maximum intensity projections from Time-of-Flight MRI scans were analyzed using different measurements of FD, the Box-counting dimension, the Minkowski dimension and generalized dimensions evaluated by means of multifractal analysis. The physiological significance of this parameter was investigated by comparing values of FD first, with the maximum slope of contrast media transit obtained from dynamic contrast-enhanced MRI data and second, with the nidus size obtained from X-ray angiography data. We found that for all methods, the Box-counting dimension, the Minkowski dimension and the generalized dimensions FD was significantly higher in the hemisphere with AVM compared to the hemisphere without AVM indicating that FD is a sensitive parameter to capture vascular complexity. Furthermore we found a high correlation between FD and the maximum slope of contrast media transit and between FD and the size of the central nidus pointing out the physiological relevance of FD. The proposed method may therefore serve as an additional objective parameter, which can be assessed automatically and might assist in the complex workup of AVMs.
AuthorsGernot Reishofer, Karl Koschutnig, Christian Enzinger, Franz Ebner, Helmut Ahammer
JournalPloS one (PLoS One) Vol. 7 Issue 7 Pg. e41148 ( 2012) ISSN: 1932-6203 [Electronic] United States
PMID22815946 (Publication Type: Journal Article)
Topics
  • Adult
  • Aged
  • Case-Control Studies
  • Cerebral Angiography (methods)
  • Cerebral Veins (pathology)
  • Cerebrovascular Circulation
  • Female
  • Fractals
  • Humans
  • Image Processing, Computer-Assisted (methods)
  • Intracranial Arteriovenous Malformations (diagnosis, pathology)
  • Magnetic Resonance Imaging (methods)
  • Male
  • Middle Aged
  • Models, Statistical
  • Neurology (methods)
  • Regression Analysis

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