title: Improving the efficiency of abdominal aortic aneurysm wall stress computations.
abstract: An abdominal aortic aneurysm is a pathological dilation of the abdominal aorta, which carries a high mortality rate if ruptured. The most commonly used surrogate marker of rupture risk is the maximal transverse diameter of the aneurysm. More recent studies suggest that wall stress from models of patient-specific aneurysm geometries extracted, for instance, from computed tomography images may be a more accurate predictor of rupture risk and an important factor in AAA size progression. However, quantification of wall stress is typically computationally intensive and time-consuming, mainly due to the nonlinear mechanical behavior of the abdominal aortic aneurysm walls. These difficulties have limited the potential of computational models in clinical practice. To facilitate computation of wall stresses, we propose to use a linear approach that ensures equilibrium of wall stresses in the aneurysms. This proposed linear model approach is easy to implement and eliminates the burden of nonlinear computations. To assess the accuracy of our proposed approach to compute wall stresses, results from idealized and patient-specific model simulations were compared to those obtained using conventional approaches and to those of a hypothetical, reference abdominal aortic aneurysm model. For the reference model, wall mechanical properties and the initial unloaded and unstressed configuration were assumed to be known, and the resulting wall stresses were used as reference for comparison. Our proposed linear approach accurately approximates wall stresses for varying model geometries and wall material properties. Our findings suggest that the proposed linear approach could be used as an effective, efficient, easy-to-use clinical tool to estimate patient-specific wall stresses.
authors.affiliation: Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Jaime E
authors.initials: JE
authors.lastname: Zelaya
authors.affiliation: Department of Mechanical Engineering, Texas A & M University, College Station, Texas, United States of America.
authors.forename: Sevan
authors.initials: S
authors.lastname: Goenezen
authors.affiliation: Department of Surgery, Division of Vascular Surgery, Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Phong T
authors.initials: PT
authors.lastname: Dargon
authors.affiliation: Department of Surgery, Division of Vascular Surgery, Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Amir-Farzin
authors.initials: AF
authors.lastname: Azarbal
authors.affiliation: Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, United States of America.
authors.forename: Sandra
authors.initials: S
authors.lastname: Rugonyi
mesh_terms: D000465:Algorithms; D001012:Aorta, Abdominal / Q000473:pathology* / Q000503:physiopathology; D017544:Aortic Aneurysm, Abdominal / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D009131:Muscle, Smooth, Vascular / Q000473:pathology / Q000503:physiopathology; D058996:Quality Improvement; D012306:Risk; D018570:Risk Assessment; D012720:Severity of Illness Index; D013312:Stress, Physiological