Background: It is believed that mechanical stresses play an important role in atherosclerotic plaque rupture process and may be used for better plaque vulnerability assessment and rupture risk predictions. Image-based plaque models have been introduced in recent years to perform mechanical stress analysis and identify critical stress indicators which may be linked to rupture risk. However, large-scale studies based on in Vivo patient data combining mechanical stress analysis, plaque morphology and composition for carotid plaque vulnerability assessment are lacking in the current literature. Methods: 206 slices of in Vivo magnetic resonance image (MRI) of carotid atherosclerotic plaques from 20 patients (age: 49-71, mean: 67.4; all male) were acquired for model construction. Modified Mooney-Rivlin models were used for vessel wall and all plaque components with parameter values chosen to match available data. A morphological plaque severity index (MPSI) was introduced based on in Vivo plaque morphological characteristics known to correlate with plaque vulnerability. Critical stress, defined as the maximum of maximum-principal-stress (Stress-P(1)) values from all possible vulnerable sites, was determined for each slice for analysis. A computational plaque stress index (CPSI, with 5 grades 0-4, 4 being most vulnerable) was defined for each slice using its critical stress value and stress interval for each CPSI grade was optimized to reach best agreement with MPSI. Correlations between CPSI and MPSI, plaque cap thickness, and lipid core size were analyzed. Results: Critical stress values correlated positively with lipid core size (r = 0.3879) and negatively with cap thickness (r = -0.3953). CPSI classifications had 71.4% agreement with MPSI classifications. The Pearson correlation coefficient between CPSI and MPSI was 0.849 (p < 0.0001). Using global maximum Stress-P(1) value for each slice to define a global maximum stress-based CPSI (G-CPSI), the agreement rate with MPSI was only 34.0%. The Pearson correlation coefficient between G-CPSI and MPSI was 0.209. Conclusion: Results from this in Vivo study demonstrated that localized critical stress values had much better correlation with plaque morphological features known to be linked to plaque rupture risk, compared to global maximum stress conditions. Critical stress indicators have the potential to improve image-based screening and plaque vulnerability assessment schemes.
Available at: http://works.bepress.com/xueying_huang/1/