Three-dimensional Finite Element Modeling of Dilated Human Ascending Aorta

A dilated ascending aorta or aortic root is susceptible to fatal aortic dissection or rupture. This risk may be attributed to increased circumferential stresses due to an increase in aortic lumen diameter. However, the effect of complex geometry on wall stresses in the dilated ascending aorta is not well understood. In this study, we combined pre-operative MRI data and measured mechanical properties of excised tissue to create a patient-specific three-dimensional finite element (FE) model of a dilated ascending aorta. Pressure loading conditions were applied using the brachial cuff pressure for the patient and physiologic boundary conditions were prescribed. The model-predicted maximum circumferential stresses occurred distal to the maximum diameter on the inner curvature of the aorta (left posterior wall). Maximum axial stresses occurred on the outer curvature (right anterior wall) near the proximal end of the aorta. The spatial variations in the magnitude of stress components suggest that the complex three-dimensional shape of dilated ascending aorta may be important in determining the risk of rupture. METHODS Informed consent was obtained from a 55-year-old male with a dilated ascending aorta undergoing elective graft replacement of the ascending aorta. A pre-operative magnetic resonance imaging (MRI) scan was used to obtain FE model geometry and non-linear elastic properties were obtained from planar biaxial testing of an aortic tissue specimen removed from the patient during surgery (Okamoto et al. 2002). Model Geometry An MRI scan was carried out one day before surgery using a 1.5T whole body scanner (ACS-NT15, Philips Medical Systems). Brachial cuff pressure was monitored periodically during the scan. After locating the position of the aorta, multiple axial cross-sections were obtained with a sagittal oblique survey. Transverse cine scans were used to determine the transverse dimensions of the aorta at different positions along its axis. The contours of multiple sagittal oblique and transverse images were digitized using MATLAB, transformed to 3-D coordinates, and combined into a single data set. A smooth surface was created from this data using a surface-fitting program (Grimm et al. 2002). This surface represents the aortic lumen under physiologic loading. In order to generate unloaded geometry, we fit the central axis of aorta to a cubic 3-D spline and determined the radius at 22 axial positions. We then divided the length of each spline segment by an initial axial scaling factor of 1.2, based on measurements of axial retraction made by Learoyd and Taylor (1966). We divided the radius at each axial position by an initial radial scaling factor of 1.5, estimated using a cylindrical model (Peterson and Okamoto 2000). We used these unloaded model dimensions and a uniform wall thickness of 2.57 mm that was measured from the biaxial test specimen to generate a 3-D solid model (SolidEdge v9, EDS Inc.). The unloaded model geometry was adjusted iteratively by changing the axial and radial scaling factors as explained below. The solid model was meshed with 80 axial, 48 circumferential and 4 radial elements using TrueGrid v 2.1.5 (XYZ Scientific, Inc.) to create a mesh with 15360 eight-noded hexahedral elements.