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Related Experiment Video

Updated: Mar 9, 2026

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

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.

BMC Medical Imaging

|December 22, 2016

Summary

This summary is machine-generated.

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.

Keywords:

B-spline curve Circle of Willis Geometric factors Quantitative analysis Skeleton Stochastic analysis

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Published on: May 4, 2015

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.