Cosmology Inspired Design of Biomimetic Tissue Engineering Templates with Gaussian Random Fields
Tissue engineering integrates the principles of engineering and life sciences toward the design, construction, modification and growth of biological substitutes that restore, maintain, or improve tissue function. The structural integrity and ultimate functionality of such tissue analogs is defined by scaffolds- porous, three-dimensional "trellis-like" structures that, on implantation, provide a viable environment to regenerate damaged tissues. The orthogonal scaffold fabrication methods currently employed can be broadly classified into two categories: (a) conventional, irreproducible, stochastic techniques producing reasonably biomorphic scaffold architecture, and (b) rapidly emerging, repeatable, computer-controlled techniques producing straight edged "contra naturam" scaffold architecture. In this paper, we present the results of the first attempt in an image-based scaffold modeling and optimization strategy that synergistically exploits the orthogonal fabrication techniques to create repeatable, biomorphic scaffolds with optimal scaffold morphology. Motivated by the use of Gaussian random fields (GRF) to model cosmological structure formation, we use appropriately ordered and clipped stacks of GRF to model the three-dimensional pore-solid scaffold labyrinths. Image-based metrology, fabrication and mechanical characterization of these scaffolds reveal the possibility of enabling the previously elusive deployment of promising benchside tissue analogs to the clinical bedside.
KeywordsCovariance Function Tissue Engineering Scaffold Dice Similarity Coefficient Gaussian Random Field Structure Model Index
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