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Purpose: Intraplaque haemorrhage (IPH) is a well-known risk factor for faster plaque progression (volume increase); however, its etiology is unclear. We aimed at determining what other local plaque- and systemic factors contribute to plaque progression and to the development and progression of IPH., Methods: We examined 98 asymptomatic participants with carotid plaque using serial multi-contrast magnetic resonance imaging. We measured the percent of wall volume (%WV=100 x [wall volume] / [total vessel volume]) and measured IPH and calcification volumes. We used generalized estimating equations-based regression to analyze predictors of %WV change and new IPH while accounting for covariates (sex, age and statin use), and multiple non-independent observations per participant., Results: Total follow-up was 1.8 ± 0.8 years on average. The presence of IPH (β: 0.6 %/y, p = 0.033) and calcification (β: 1.2 %/y, p = 0.028) were each associated with faster plaque progression. New IPH, detected on a subsequent scan in 4 % of arteries that did not initially have IPH, was associated with larger calcification (odds ratio [OR]: 2.6 per 1-SD increase, p = 0.038) and higher pulse pressure (OR: 2.3 per 1-SD increase, p = 0.016). Larger calcification was associated with greater increases in pulse pressure (β: 1.4 mm Hg/y per 1-SD increase, p = 0.040)., Conclusions: IPH and calcification are each independently associated with faster plaque progression. The association of carotid calcification to increased pulse pressure and new IPH development suggests a possible mechanism by which calcification drives IPH development and plaque progression., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.