Optimizing the Porohyperelastic Response of a Layered Compliance Matched Vascular Graft to Promote Luminal Self-Cleaning.

Thrombosis and intimal hyperplasia have remained the major failure mechanisms of small-diameter vascular grafts used in bypass procedures. While most efforts to reduce thrombogenicity have used a biochemical surface modification approach, the use of local mechanical phenomena to aid in this goal has received somewhat less attention. In this work, the mechanical, fluid transport, and geometrical properties of a layered and porous vascular graft are optimized within a porohyperelastic finite element framework to maximize self-cleaning via luminal reversal fluid velocity (into the lumen). This is expected to repel platelets as well as inhibit the formation of and/or destabilize adsorbed protein layers thereby reducing thrombogenic potential. A particle swarm optimization algorithm was utilized to maximize luminal reversal fluid velocity while also compliance matching our graft to a target artery (rat aorta). The maximum achievable luminal reversal fluid velocity was approximately 246 µm/s without simultaneously optimizing for host compliance. Simultaneous optimization of reversal flow and compliance resulted in a luminal reversal fluid velocity of 59 µm/s. Results indicate that a thick highly permeable compressible inner layer and a thin low permeability incompressible outer layer promote intraluminal reversal fluid velocity. Future research is needed to determine the feasibility of fabricating such a layered and optimized graft and verify its ability to improve hemocompatibility.

Three-Dimensional Segmentation of the Ex-Vivo Anterior Lamina Cribrosa From Second-Harmonic Imaging Microscopy.

The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated using mutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.