ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

chamber [4].
After obtaining the vascularization within the scaffolds it is necessary to quantify this
vasculature. Some approximations have been made to quantify the total volume of the
capillary network within the scaffolds [5] characterizing µ-vessel within scaffolds in
physiological in vivo tissue engineering implant context. So far, there are only
functional analysis of large vessels and simple networks [6], [7] like Lee et al. [8] have
done whit their study of a 3D computational fluid flow modeling of a wide angle
bifurcation to analyze WSS, blood velocity, and blood pressure profiles in a chicken
embryo model. However, qualitative studies of growth and performance of these µvessels
and complex vascular networks within the scaffolds to report velocity, pressure
and wall shear stress (WSS) profiles to determine and analyze their functionality have
not been done yet.
Measure of WSS, velocity or pressure within tissue constructs to evaluate the
functionality of µ-vascular networks in vitro or in vivo is difficult and expensive. Gödde
and Kurz [9] modeled blood flow through capillary networks to determine local
pressure gradients, which were in turn used to calculate local shear stress. Also blood
flow, pressures and shear stresses on ECs have been proposed and modeled as
angiogenic stimuli [10]. Therefore, to perform a quantitative assessment of the
functionality of the complex µ-vascular networks based on the numerical simulations
turns out to save significant time and cost experiment and can be a useful support tool
for predicting the functionality of these new vascular networks within the scaffolds in
tissue engineering design.
3. METHODS
3.1 Image acquisition
Using the corrosion casting method [11], complex vascular networks formed in different
tissues of mice were obtained. One of the main advantages of this method is that it
allows the quantification of the vascular network down to the capillary level. The data
in DICOM (DC) format were provided by the Swiss federal Institute of Technology
Zurich ETHZ using the Synchrotron at the Swiss Light Source (PSI, Villigen,
Switzerland) with a resolution of 1.4 µm and the µ-computed tomography scanner (µCT
50 Scanco Medical AG, Bruttisellen, Switzerland) with a resolution of 3 µm
3.2 Image segmentation and 3D reconstruction
Vascular segmentation and 3D reconstruction of the networks geometry were
accomplished by Thresholding and FloodFill within the Simpleware (v4.3) image
processing software. A vascular network taken from the proximal part of the mouse tail
and from the lower hind limb were segmented and reconstructed. Also, in order to
analyze the functionality of the vascular network in more detail, a sub-volume of 5% of
the tail network was taken and the same condition was applied on this sub-sample.
During the segmentation process it was ensure an interconnected network. Only the
connected elements of the geometry resulting of the segmentation process were used to
the 3D reconstruction and the non-connected structures were removed to proceed with
the construction of the superficial and volumetric mesh.