Abstract
BACKGROUND: Carotid atherosclerosis is a major contributor to the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke risk and plaque burden, its long-term effects on plaque dynamics remain unclear. This study aimed to evaluate the long-term impact of IPH on carotid plaque burden progression using deep learning-based segmentation on multi-contrast magnetic resonance vessel wall imaging (VWI). METHODS: 28 asymptomatic subjects with carotid atherosclerosis underwent an average of 4.7±0.6 VWI scans over 5.8±1.1 years. Deep learning pipelines were used to segment the carotid vessel walls and IPH. Bilateral plaque progression was analyzed using correlation coefficients and generalized estimating equations. Associations between IPH occurrence, IPH volume, and plaque burden (%WV) progression were evaluated using linear mixed-effect models. RESULTS: IPH was detected in 23/50 of the arteries at any time point. Of arteries without IPH at baseline, 11/39 developed new IPH that persisted, while 5/11 arteries with baseline IPH exhibited it throughout the study. Bilateral plaque growth was significantly correlated (r = 0.54, p<0.001), but this symmetry was weakened in cases with IPH (r = 0.1, p = 0.62). Moreover, IPH presence or development at any point was associated with a 2.3% absolute increase in %WV on average within the affected artery (p<0.001). The volume of IPH was also positively associated with increased %WV (p = 0.005). CONCLUSION: Deep learning-based segmentation pipelines were utilized to identify IPH, quantify IPH volume, and measure their effects on carotid plaque burden during long-term follow-up. Findings demonstrated that IPH may persist for extended periods. While arteries without IPH demonstrated minimal progression under contemporary treatment, the presence of IPH and greater IPH volume significantly accelerated long-term plaque growth.