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Updated: Oct 7, 2025

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
Published on: July 28, 2018
Quantitative assessment of microstructural evolution of intracranial aneurysm wall by vessel wall imaging
Hidenori Endo1,2, Naoko Mori3, Shunji Mugikura4,5
1Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Japan.
Purpose:
This study aimed to evaluate new quantitative parameters of aneurysm wall enhancement (AWE) on magnetic resonance vessel wall imaging (VWI) in differentiating between the stable and evolving unruptured intracranial aneurysms (UIAs).
Methods:
Thirty-eight consecutive patients with UIAs (27 stable and 11 evolving) underwent VWI with contrast-enhanced 3D T1 volume isotropic turbo spin echo acquisition. The voxel-based enhancement maps were created using pre- and post-contrast images. The aneurysmal lumen with signal suppression by black-blood method was segmented. Then, one voxel outer and inner layers of the lumen contour were automatically segmented. The shape features of the aneurysms and AWE of the two layers were compared between stable and evolving groups.
Results:
The shape features, including aneurysm volume, surface, and compacity were significantly different between the stable and evolving groups (P = 0.024, 0.028, and 0.033, respectively). Stable and evolving groups also differed significantly in the AWE at the union of outer and inner layers of the aneurysm wall (P = 0.0082) but not in that of the outer or inner layer alone. Multivariate logistic regression analysis revealed significant differences in aneurysm volume, surface, and AWE at the union of outer and inner layers between the two groups (P = 0.0029, 0.0092, and 0.0033, respectively). Receiver operating characteristics curve analysis revealed that the area under the curve of the logistic regression model was 0.89.
Conclusion:
Quantitative combined analysis of aneurysm shape features and AWE of the union of outer and inner layers were effective for differentiating between stable and evolving UIAs.

