Abstract

BackgroundStomach-related disorders impose medical challenges and are associated with significant social and economic costs. The field of biomechanics is promising for understanding tissue behavior and for development of medical treatments and surgical interventions. In gastroenterology, animal models are often used when studies on humans are not possible. Often large animal models with similar anatomical characteristics (size and shape) are preferred. However, it is uncertain if stomachs from humans and large animals have similar mechanical properties. The aim of the present study is to characterize and compare hyper- and viscoelastic properties of porcine and human gastric tissue using tension and radial compression tests. Methods: Hyperelastic and viscoelastic properties were quantified from quasi-static ramp tests and stress relaxation tests. Tension in two directions and radial compression experiments were done on intact stomach wall samples as well as on separated mucosa and muscularis layer samples from porcine and human fundus, corpus and antrum. Results and conclusions: Similar hyper- and viscoelastic constitutive models can be used to describe porcine and human gastric tissue. In total, 19 constitutive parameters were compared and results showed significant variations between species. For example, for intact circumferential samples from antrum, the stiffness (a) and relaxation (τ1) were greater for human samples than for porcine samples (p < 0.0001). The constitutive parameters were condition-, region- and layer-dependent and no distinct pattern hereof between species was found. This indicates that different parameters must be used to describe the specific situation. The present work provides insight into porcine and human gastric radial compressive and tensile hyper- and viscoelastic properties, strengthening the inter-species relation of the biomechanical properties. Constitutive relations were established that may aid development and translation of diagnostic or therapeutic devices with computational models.

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