An automated algorithm to improve the precision of basilar artery diameter measurements before and after subarachnoid hemorrhage-induced vasospasm in an animal model.

Objective: Quantifying vasospasm has traditionally been performed manually, a method prone to imprecision and user bias. An alternative approach is to use computerized image analysis techniques to define and quantify the diameter of a vessel. The goal of this article is to demonstrate a novel automated vessel measurement algorithm specific to the needs of vasospasm studies and to compare it with traditional manual measurements in an animal model of vasospasm.

Methods: A total of 576 arterial diameter measurements were collected by 4 independent, blinded examiners from 24 angiograms in a rabbit subarachnoid hemorrhage (SAH) model. Measurements were taken from 3 segments of the basilar artery in anteroposterior and lateral projections, both before SAH and after SAH-induced vasospasm. Means and standard deviations of 288 manual measurements were compared with 288 automated measurements.

Results: The precision of automated measurements was significantly improved compared with standardized manual measurements (85.7% decrease in variation; P < .001). When using automated measurements, the precision was not affected by vessel size, but when using manual measurements, smaller arteries were less precise (P = .04). There was no significant difference in precision between 2 different contrast concentrations (P = .32).

Conclusion: Automated measurements of basilar artery diameters are more precise than manual measurements, both before and after SAH-induced vasospasm. The variability in the manual group worsens when the artery is smaller secondary to vasospasm, indicating a need for the use of this segmentation method.