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. 2016 Dec 28;15(Suppl 2):156.
doi: 10.1186/s12938-016-0270-2.

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

Ruoyu Xing  1 , David De Wilde  2 , Gayle McCann  1 , Yanto Ridwan  3 , Jelle T C Schrauwen  1 , Anton F W van der Steen  1 , Frank J H Gijsen  4 , Kim Van der Heiden  1
Affiliations

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

Ruoyu Xing et al. Biomed Eng Online. .

Abstract

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.

Keywords: Atherosclerosis; Contrast media; Image segmentation; Micro-CT imaging; Wall shear stress.

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Figures

Fig. 1
Fig. 1
Illustration of image segmentation. Red straight line indicates the original contour placed in MeVisLab; Lumen area is represented by the red shaded area by shrinking 20% of the original contour area; Background area is defined as the area between the 20 and 40% expanded original contour, shown as the green shaded area
Fig. 2
Fig. 2
3D volume rendering of the bone structures and vascular network in the neck region for eXIA 160. Major vasculature including internal jugular vein (①) and the carotid arteries (②) were visualized (scale bar 1 mm). The box shows the zoomed-in area of the narrowing RCCA caused by the tapering cast (scale bar 400 µm)
Fig. 3
Fig. 3
a Illustration of the RCCA with cast on micro-CT using eXIA 160. b Contrast intensity along the RCCA using eXIA 160. Lumen region is shown in red line with square markers; Blue line with cross markers represents the local threshold; Black dashed line indicates global threshold value; Background region is marked as green line with circles; Standard deviation is indicated by shadow areas
Fig. 4
Fig. 4
3D reconstruction of RCCA by eXIA 160 enhanced micro-CT using a the local threshold and b the global threshold segmentation. The narrowing of the lumen caused by the cast was clearly captured
Fig. 5
Fig. 5
a Comparison of RCCA vessel dimensions obtained from two segmentation methods with known cast geometry. Vessel surface reconstructed from the local threshold segmentation (straight line) showed good agreement between the vessel geometry and the cast dimension. Vessel surface reconstructed from the global threshold segmentation (dashed line) significantly underestimated the cast dimensions. b RCCA vessel wall thickness within the cast region obtained from the local threshold segmentation (straight line) corresponded to that of the normal vessel wall thickness. Vessel surface reconstructed from the global threshold segmentation (dashed line) significantly overestimated the vessel wall thickness
Fig. 6
Fig. 6
Time-dependent velocity profile at proximal RCCA. Black dash lines indicate wave forms measured by Doppler Ultrasound; Red line represents the averaged velocity calculated over 4 cardiac cycles while blue lines indicates the filtered wave form used as inlet boundary condition
Fig. 7
Fig. 7
a Hemodynamic analysis of shear-manipulated RCCA using vessel geometry created from the local threshold method. Streamline (left panel) representing velocity field along the RCCA; TAWSS (middle panel) and OSI distribution (right panel) along the RCCA. b Hemodynamic analysis of shear-manipulated RCCA using vessel geometry created from the global threshold method. Streamline (left panel) representing velocity field along the RCCA; TAWSS (middle panel) and OSI distribution (right panel) along the RCCA. The range of the colormap is between 0 and 20 Pa
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