Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.

In this work, non-invasive high-spatial resolution three-dimensional (3D) X-ray micro-computed tomography (µCT) of healthy mouse lung vasculature is performed. Methodologies are presented for filtering, segmenting, and skeletonizing the collected 3D images. Novel methods for the removal of spurious branch artefacts from the skeletonized 3D image are introduced, and these novel methods involve a combination of distance transform gradients, diameter-length ratios, and the fast marching method (FMM). These new techniques of spurious branch removal result in the consistent removal of spurious branches without compromising the connectivity of the pulmonary circuit. Analysis of the filtered, skeletonized, and segmented 3D images is performed using a newly developed Vessel Network Extraction algorithm to fully characterize the morphology of the mouse pulmonary circuit. The removal of spurious branches from the skeletonized image results in an accurate representation of the pulmonary circuit with significantly less variability in vessel diameter and vessel length in each generation. The branching morphology of a full pulmonary circuit is characterized by the mean diameter per generation and number of vessels per generation. The methods presented in this paper lead to a significant improvement in the characterization of 3D vasculature imaging, allow for automatic separation of arteries and veins, and for the characterization of generations containing capillaries and intrapulmonary arteriovenous anastomoses (IPAVA).

Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization.

We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demonstrated a 73% reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments based on common clinical practices. We enhanced the uniformity of cell distributions in the lung vasculature by increasing the injection flow rate from 3.81 ml/min to 9.40 ml/min. As a result, the cell seeding efficiency increased in both the numerical and experimental results by 42 and 66%, respectively.