ArticleCardiovascular engineering and technology2026
Sources of Variability in Intracranial Aneurysm Model Reconstruction and Evaluation: A Systematic Investigation.
Article in Cardiovascular engineering and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe intracranial aneurysm (IA) model reconstruction is critical for pathophysiology diagnosis and computational simulations. This study aimed to quantify the impact of segmentation thresholds and software platforms on the reconstruction of IA geometry as well as the impact of inter-user variability on the assessment of morphology.
methods600 IA models were reconstructed from 100 patient DSA datasets using Materialise Mimics and 3D Slicer at three grey value (GV) thresholds; 1000, 1500, and 2500. Geometric measurements were performed in 3-matic by three users. Measurements included vessel diameters and aneurysm morphology parameters. Mimics, the 2500 GV threshold, and the most experienced user (R1) served as baselines for comparison. Normality was evaluated using Shapiro-Wilk tests, and statistical differences were assessed with paired t-tests and relative percent differences.
resultsAll anatomical regions showed statistically significant geometric variation across software and threshold. Model evaluation showed potential statistically significant variation between users. Models from 3D Slicer were consistently smaller than those from Mimics with percent differences ranging from - 1.27 to - 4.38% (all p < .05). Lower thresholds produced consistently larger models; decreasing from 2500 to 1000 GV increased average diameters by up to 15.9%, depending on specific region. User-related variability was most pronounced in the least experienced user, with size measurements deviating by up to 22.67% from the baseline.
conclusionSegmentation software, threshold selection, and user interaction each introduce meaningful and statistically significant variability into IA model geometry and evaluation. Standardization of segmentation protocols-especially threshold values and operator training-is essential to improve reproducibility.
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