Model-based cap thickness and peak cap stress prediction for carotid MRI

A rupture-prone carotid plaque can potentially be identified by calculating the peak cap stress (PCS). For these calculations, plaque geometry from MRI is often used. Unfortunately, MRI is hampered by a low resolution, leading to an overestimation of cap thickness and an underestimation of PCS. We developed a model to reconstruct the cap based on plaque geometry to better predict cap thickness and PCS. We used histological stained plaques from 34 patients. These plaques were segmented and served as the ground truth. Sections of these plaques contained 93 necrotic cores with a cap thickness <0.62 mm which were used to generate a geometry-based model. The histological data was used to simulate in vivo MRI images, which were manually delineated by three experienced MRI readers. Caps below the MRI resolution (n = 31) were (digitally removed and) reconstructed according to the geometry-based model. Cap thickness and PCS were determined for the ground truth, readers, and reconstructed geometries. Cap thickness was 0.07 mm for the ground truth, 0.23 mm for the readers, and 0.12 mm for the reconstructed geometries. The model predicts cap thickness significantly better than the readers. PCS was 464 kPa for the ground truth, 262 kPa for the readers and 384 kPa for the reconstructed geometries. The model did not predict the PCS significantly better than the readers. The geometry-based model provided a significant improvement for cap thickness estimation and can potentially help in rupture-risk prediction, solely based on cap thickness. Estimation of PCS estimation did not improve, probably due to the complex shape of the plaques

The impact of helical flow on coronary atherosclerotic plaque development

Background and aims: Atherosclerosis has been associated with near-wall hemodynamics and wall shear stress (WSS). However, the role of coronary intravascular hemodynamics, in particular of the helical flow (HF) patterns that physiologically develop in those arteries, is rarely considered. The purpose of this study was to assess how HF affects coronary plaque initiation and progression, definitively demonstrating its atheroprotective nature. Methods: The three main coronary arteries of five adult hypercholesterolemic mini-pigs on a high fat diet were imaged by computed coronary tomography angiography (CCTA) and intravascular ultrasound (IVUS) at 3 (T1, baseline) and 9.4 ± 1.9 (T2) months follow-up. The baseline geometries of imaged coronary arteries (n = 15) were reconstructed, and combined with pig-specific boundary conditions (based on in vivo Doppler blood flow measurements) to perform computational fluid dynamic simulations. Local wall thickness (WT) was measured on IVUS images at T1 and T2, and

Computational Biomechanics of Diseased Arteries

Computational modelling is a powerful tool to identify stresses in diseased arteries and predict which artery or plaque is at immediate or future risk of a cardiovascular event. In this thesis, new methodologies have been developed to further improve computational modelling in diseased arteries. These new methodologies enabled wall stress analysis in AAA geometries obtained via 3D-US and in plaque geometries obtained via combined OCT and IVUS imaging. Further, multidirectional shear stress parameters proved to be importantly affecting plaque composition. Moreover, shear stress in combination with NIRS positive regions produced promising results for predicting plaque progression and composition changes. Therefore, multidirectional shear stress, perhaps in combination with NIRS, could be used as a potential risk factor for plaque progression and changes in plaque composition. Before these factors can be clinically employed, future research should further elucidate the clinical value of shear stress and wall stress as a predictor of changes in disease stage and cardiovascular events