A Computational Model of Biophysical Properties of the Rat Stomach Informed by Comprehensive Analysis of Muscle Anatomy

An anatomically based 3D computational model of the rat stomach was developed using experimental muscle thickness measurements and muscle fiber orientations for the longitudinal muscle (LM) and circular muscle (CM) layers. First, 15 data points corresponding to the measurements were registered on the dorsal and ventral faces of the serosal surface of an averaged 3D rat stomach model. A thickness field representing the varying wall thickness was fitted to the surface and nodal points were projected outwards (for the LM layer) and inwards (for the CM layer) to create 2 new surfaces. In addition, a computational volume mesh was created and fiber orientation in each tetrahedral element was computed using a Laplace-Dirichlet rule-based algorithm and a simulation was performed to validate the model. The stomach model successfully represented the experimental measurements with a thickness in the range of 11.7–52.9 µm and 40.6–276.5 µm in the LM and CM layers, respectively, while the variation across the stomach was in agreement with the reported values. Similarly, the generated fiber orientations matched with the investigated fiber data and successfully resembled the observed properties such as the hairpin-like structure formed by the LM fibers in the fundus. Bioelectrical simulation using the developed model was successfully converged and reflected the properties of normal antegrade activity. In conclusion, a 3D computational model of the rat stomach was successfully developed and tested for in-silico studies. The model will be used in future studies to assess parameters in electrical therapies and to investigate the structure-function relationship in gastric motility. Clinical Relevance - Electrical stimulation is an emerging therapy for functional motility disorders. The 3D model of rat stomach developed in this study could provide accurate assessment of the efficacy of a vast range of stimulation parameters via in-silico studies and could aid in the adaptation of electrical therapies to clinical settings.