Background and aims: Atherosclerotic plaque onset and progression are known to be affected by local biomechanical factors. While the role of wall shear stress (WSS) has been studied, the impact of another biomechanical factor, namely mechanical wall stress (MWS), remains poorly understood. In this study, we investigated the association of MWS, independently and combined with WSS, towards atherosclerosis in coronary arteries. Methods: Thirty-four human coronary arteries were analyzed using near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) at baseline and after 12 months. Baseline WSS and MWS were calculated using computational models, and wall thickness (ΔWT) and lipid-rich necrotic core size (ΔLRNC) change were measured in non-calcified coronary segments. The arteries were further divided into 1.5 mm/45° sectors and categorized as plaque-free or plaque sectors. For each category, associations between biomechanical factors (WSS & MWS) and changes in coronary wall (ΔWT & ΔLRNC) were studied using linear mixed models. Results: In plaque-free sectors, higher MWS (p < 0.001) was associated with greater vessel wall growth. Plaque sectors demonstrated wall thickness reduction over time, likely due to medical therapy, where higher levels of WSS and WMS, individually and combined, (p < 0.05) were associated with a greater reduction. Sectors with low MWS combined with high WSS demonstrated the highest LRNC increase (p < 0.01). Conclusions: In this study, we investigated the association of the (largely-overlooked) biomechanical factor MWS with coronary atherosclerosis, individually and combined with WSS. Our results demonstrated that both MWS and WSS significantly correlate with atherosclerotic plaque initiation and development.

Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.