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Full understanding of strain-induced collagen organization in complex tissue geometries to create tissues with predefined collagen architecture has not been achieved. This is mainly due to our limited knowledge of collagen remodeling in developing tissues. Here we investigate straininduced collagen (re)organization in fibrin based engineered tissues using nondestructive time-lapse imaging. The tissues start from a biaxially constrained myofibroblast-populated fibrin gel and are used to study: (A) remodeling from a static equi-biaxial loading condition to static uniaxial loading; and (B) remodeling of a biaxially constrained tissue under uniaxial cyclic straining before and after a change in strain direction. Under static conditions, collagen oriented parallel to the direction of strain, whereas under cyclic conditions the orientation in the constrained tissue was perpendicular to the direction of strain. It is concluded that due to the biaxial constraints the uniaxially, cyclically strained cells can exert forces in two directions and strain shield themselves. A subsequent change in the direction of cyclic straining resulted in a rapid reorientation of collagen at the tissue surface. Reorientation was significantly slower in deeper tissue layers, where tissue remodeling was dominated by contact guidance provided by the endogenous matrix. These findings emphasize the relevance of achieving a functional collagen organization right from the start of tissue culture.

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Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given.

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