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Updated: Mar 9, 2026
A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
Published on: July 28, 2018
Skeleton-based cerebrovascular quantitative analysis
Xingce Wang1, Enhui Liu1, Zhongke Wu2
1College of Information Science and Technology, Beijing Normal University, Beijing, China.
Insights
This study introduces a new automated method for analyzing the Circle of Willis (CoW) geometry, crucial for understanding cerebrovascular diseases like stroke. The stable methodology enables quantitative analysis of CoW structures in large populations.
Area of Science:
- Medical Imaging Analysis
- Computational Anatomy
- Cerebrovascular Research
Background:
- Cerebrovascular diseases, including stroke, are leading global causes of mortality.
- Understanding geometric factors of cerebrovascular structures is vital for diagnosis and pathology.
- Quantitative analysis of the Circle of Willis (CoW) is essential for identifying disease-related changes.
Purpose of the Study:
- To develop a stable and consistent methodology for quantitative Circle of Willis (CoW) analysis.
- To identify geometric changes within the CoW structure using advanced computational methods.
- To establish an automated pipeline for analyzing large medical image datasets of the CoW.
Main Methods:
- An automated pipeline was designed, incorporating stochastic segmentation for improved volumetric data acquisition.
- The L1 medial axis method was applied to vessel volumetric data to generate discrete skeleton datasets.
- B-spline curves were utilized to fit the skeleton, enabling the calculation of geometric values (length, curvature, torsion, radius, angle).
Main Results:
- Quantitative geometric values for CoW branches were calculated, with specific data provided for vessel length, curvature, torsion, radius, and angle.
- The anterior communicating artery (ACo) was identified as the shortest vessel (2.6mm).
- Correlations were observed between the radii of symmetrical posterior cerebral arteries (PCA) and the angles of symmetrical posterior communicating arteries (PCo), validating the method's stability.
Conclusions:
- The developed methodology demonstrated stability and suitability for analyzing large medical image datasets from automated pipelines.
- The quantitative analysis of CoW geometry provides valuable insights into cerebrovascular structures.
- The method is adaptable for analyzing other tubular organs, such as the large intestine and bile duct.
Background:
Cerebrovascular disease is the most common cause of death worldwide, with millions of deaths annually. Interest is increasing toward understanding the geometric factors that influence cerebrovascular diseases, such as stroke. Cerebrovascular shape analyses are essential for the diagnosis and pathological identification of these conditions. The current study aimed to provide a stable and consistent methodology for quantitative Circle of Willis (CoW) analysis and to identify geometric changes in this structure.
Method:
An entire pipeline was designed with emphasis on automating each step. The stochastic segmentation was improved and volumetric data were obtained. The L1 medial axis method was applied to vessel volumetric data, which yielded a discrete skeleton dataset. A B-spline curve was used to fit the skeleton, and geometric values were proposed for a one-dimensional skeleton and radius. The calculations used to derive these values were illustrated in detail.
Result:
In one example(No. 47 in the open dataset) all values for different branches of CoW were calculated. The anterior communicating artery(ACo) was the shortest vessel, with a length of 2.6mm. The range of the curvature of all vessels was (0.3, 0.9) ± (0.1, 1.4). The range of the torsion was (-12.4,0.8) ± (0, 48.7). The mean radius value range was (3.1, 1.5) ± (0.1, 0.7) mm, and the mean angle value range was (2.2, 2.9) ± (0, 0.2) mm. In addition to the torsion variance values in a few vessels, the variance values of all vessel characteristics remained near 1. The distribution of the radii of symmetrical posterior cerebral artery(PCA) and angle values of the symmetrical posterior communicating arteries(PCo) demonstrated a certain correlation between the corresponding values of symmetrical vessels on the CoW.
Conclusion:
The data verified the stability of our methodology. Our method was appropriate for the analysis of large medical image datasets derived from the automated pipeline for populations. This method was applicable to other tubular organs, such as the large intestine and bile duct.
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