A Computational Growth Framework for Biological Tissues: Application to Growth of Aortic Root Aneurysm Repaired by the V-shape Surgery

Abstract

AbstractAscending aortic aneurysms (AsAA) often include the dilatation of sinotubular junction (STJ) which usually leads to aortic insufficiency. The novel surgery of the V-shape resection of the noncoronary sinus, for treatment of AsAA with root ectasia, has been shown to be a simpler procedure compared to traditional surgeries. Our previous study showed that the repaired aortic root aneurysms grew after the surgery. In this study, we developed a novel computational growth framework to model the growth of the aortic root repaired by the V-shape surgery. Specifically, the unified-fiber-distribution (UFD) model was applied to describe the hyperelastic deformation of the aortic tissue. A novel kinematic growth evolution law was proposed based on existing observations that the growth rate is linearly dependent on the wall stress. Moreover, we also obtained patient-specific geometries of the repaired aortic root post-surgery at two follow-up time points (Post1 and Post2) for 5 patients, based on clinical CT images. The novel computational growth framework was implemented into the Abaqus UMAT user subroutine and applied to model the growth of the aortic root from Post1 to Post2. Patient-specific growth parameters were obtained by an optimization procedure. The predicted geometry and stress of the aortic root at Post2 agree well with the in vivo results. The novel computational growth framework and the optimized growth parameters could be applied to predict the growth of repaired aortic root aneurysms for new patients and to optimize repair strategies for AsAA.