Contrast-enhanced micro-CT imaging in murine carotid arteries : a new protocol for computing wall shear stress

Background: Wall shear stress (WSS) is involved in the pathophysiology of atherosclerosis. The correlation between WSS and atherosclerosis can be investigated over time using a WSS-manipulated atherosclerotic mouse model. To determine WSS in vivo, detailed 3D geometry of the vessel network is required. However, a protocol to reconstruct 3D murine vasculature using this animal model is lacking. In this project, we evaluated the adequacy of eXIA 160, a small animal contrast agent, for assessing murine vascular network on micro-CT. Also, a protocol was established for vessel geometry segmentation and WSS analysis. Methods: A tapering cast was placed around the right common carotid artery (RCCA) of ApoE(-/-) mice (n = 8). Contrast-enhanced micro-CT was performed using eXIA 160. An innovative local threshold-based segmentation procedure was implemented to reconstruct 3D geometry of the RCCA. The reconstructed RCCA was compared to the vessel geometry using a global threshold-based segmentation method. Computational fluid dynamics was applied to compute the velocity field and WSS distribution along the RCCA. Results: eXIA 160-enhanced micro-CT allowed clear visualization and assessment of the RCCA in all eight animals. No adverse biological effects were observed from the use of eXIA 160. Segmentation using local threshold values generated more accurate RCCA geometry than the global threshold-based approach. Mouse-specific velocity data and the RCCA geometry generated 3D WSS maps with high resolution, enabling quantitative analysis of WSS. In all animals, we observed low WSS upstream of the cast. Downstream of the cast, asymmetric WSS patterns were revealed with variation in size and location between animals. Conclusions: eXIA 160 provided good contrast to reconstruct 3D vessel geometry and determine WSS patterns in the RCCA of the atherosclerotic mouse model. We established a novel local threshold-based segmentation protocol for RCCA reconstruction and WSS computation. The observed differences between animals indicate the necessity to use mouse-specific data for WSS analysis. For our future work, our protocol makes it possible to study in vivo WSS longitudinally over a growing plaque

3D Fiber Orientation in Atherosclerotic Carotid Plaques

Atherosclerotic plaque rupture is the primary trigger of fatal cardiovascular events. Fibrillar collagen in atherosclerotic plaques and their directionality are anticipated to play a crucial role in plaque rupture. This study aimed assessing 3D fiber orientations and architecture in atherosclerotic plaques for the first time.Seven carotid plaques were imaged ex-vivo with a state-of-the-art Diffusion Tensor Imaging (DTI) technique, using a high magnetic field (9.4. Tesla) MRI scanner. A 3D spin-echo sequence with uni-polar diffusion sensitizing pulsed field gradients was utilized for DTI and fiber directions were assessed from diffusion tensor measurements. The distribution of the 3D fiber orientations in atherosclerotic plaques were quantified and the principal fiber orientations (circumferential, longitudinal or radial) were determined.Overall, 52% of the fiber orientations in the carotid plaque specimens were closest to the circumferential direction, 34% to the longitudinal direction, and 14% to the radial direction. Statistically no significant difference was measured in the amount of the fiber orientations between the concentric and eccentric plaque sites. However, concentric plaque sites showed a distinct structural organization, where the principally longitudinally oriented fibers were closer to the luminal side and the principally circumferentially oriented fibers were located more abluminally. The acquired unique information on 3D plaque fiber direction will help understanding pathobiological mechanisms of atherosclerotic plaque progression and pave the road to more realistic biomechanical plaque modeling for rupture assessment

Fast and Accurate Pressure-Drop Prediction in Straightened Atherosclerotic Coronary Arteries

Atherosclerotic disease progression in coronary arteries is influenced by wall shear stress. To compute patient-specific wall shear stress, computational fluid dynamics (CFD) is required. In this study we propose a method for computing the pressure-drop in regions

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

Quantification of fibrous cap thickness in intracoronary optical coherence tomography with a contour segmentation method based on dynamic programming

OBJECTIVES: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. METHODS: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. RESULTS: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of [Formula: see text] ) and were similar to inter-observer reproducibility ([Formula: see text] , R = .74), while being significantly faster and fully reproducible. CONCLUSION: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques