3D strain measurement in soft tissue: demonstration of a novel inverse finite element model algorithm on MicroCT images of a tissue phantom exposed to negative pressure wound therapy.

This study describes a novel system for acquiring the 3D strain field in soft tissue at sub-millimeter spatial resolution during negative pressure wound therapy (NPWT). Recent research in advanced wound treatment modalities theorizes that microdeformations induced by the application of sub-atmospheric (negative) pressure through V.A.C. GranuFoam Dressing, a reticulated open-cell polyurethane foam (ROCF), is instrumental in regulating the mechanobiology of granulation tissue formation [Saxena, V., Hwang, C.W., Huang, S., Eichbaum, Q., Ingber, D., Orgill, D.P., 2004. Vacuum-assisted closure: Microdeformations of wounds and cell proliferation. Plast. Reconstr. Surg. 114, 1086-1096]. While the clinical response is unequivocal, measurement of deformations at the wound-dressing interface has not been possible due to the inaccessibility of the wound tissue beneath the sealed dressing. Here we describe the development of a bench-test wound model for microcomputed tomography (microCT) imaging of deformation induced by NPWT and an algorithm set for quantifying the 3D strain field at sub-millimeter resolution. Microdeformations induced in the tissue phantom revealed average tensile strains of 18%-23% at sub-atmospheric pressures of -50 to -200 mmHg (-6.7 to -26.7 kPa). The compressive strains (22%-24%) and shear strains (20%-23%) correlate with 2D FEM studies of microdeformational wound therapy in the reference cited above. We anticipate that strain signals quantified using this system can then be used in future research aimed at correlating the effects of mechanical loading on the phenotypic expression of dermal fibroblasts in acute and chronic ulcer models. Furthermore, the method developed here can be applied to continuum deformation analysis in other contexts, such as 3D cell culture via confocal microscopy, full scale CT and MRI imaging, and in machine vision.