Skeleton-based cerebrovascular quantitative analysis

BMC Medical Imaging

Research Article

Zhongke Wu¹  Xingce Wang¹  Mingquan Zhou¹  Wuyang Shui¹  Enhui Liu¹  Feifei Zhai²  Yi-Cheng Zhu² 

[1] College of Information Science and Technology, Beijing Normal University, Beijing, China;Department of Neurology, Peking Union Medical College Hospital, Beijing, China;

关键词: Geometric factors;    Circle of Willis;    Quantitative analysis;    B-spline curve;    Skeleton;    Stochastic analysis;   

DOI  :  10.1186/s12880-016-0170-8

 received in 2016-04-06, accepted in 2016-11-27,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundCerebrovascular 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.MethodAn 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.ResultIn 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.ConclusionThe 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.

【 授权许可】

CC BY   
© The Author(s) 2016