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Goal: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on noninvasive data. Methods: As for the characteristics, we consider a nonlinear constitutive model of the aortic wall and assume patient-specific model coefficients. Through a regression model, we build the response surfaces of ultimate stress, ultimate stretch, and model coefficients in function of patient data that are commonly available in the clinical practice. We apply the approach to a dataset of 59 patients. Results: The approach is fair and accurate response surfaces can be obtained for both ultimate properties and model coefficients. Conclusion: Prediction errors are acceptable, even though a larger patient dataset will be required to stabilize the surfaces, making it possible to apply the approach in the clinical practice. Significance: A fair prediction of the patient aortic mechanical behavior, based on clinical information noninvasively acquired, would improve the decision process and lead to more effective treatments. Goal: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on noninvasive data. Methods: As for the characteristics, we consider a nonlinear constitutive model of the aortic wall and assume patient-specific model coefficients. Through a regression model, we build the response surfaces of ultimate stress, ultimate stretch, and model coefficients in function of patient data that are commonly available in the clinical practice. We apply the approach to a dataset of 59 patients. Results: The approach is fair and accurate response surfaces can be obtained for both ultimate properties and model coefficients. Conclusion: Prediction errors are acceptable, even though a larger patient dataset will be required to stabilize the surfaces, making it possible to apply the approach in the clinical practice. Significance: A fair prediction of the patient aortic mechanical behavior, based on clinical information noninvasively acquired, would improve the decision process and lead to more effective treatments.

The degeneration of the vascular wall tissue induces a change of the arterial stiffness, i.e., the capability of the vessel to distend under the pulsatile hemodynamic load. In the literature, the aortic stiffness is usually computed following a simple deterministic approach, in which only the maximum and the minimum values of arterial diameter and blood pressure over the cardiac cycle are considered. In this paper, we propose a stochastic approach to assess the stiffness, and its spatial variation, of a given aortic region exploiting patient-specific geometrical data derived from computed tomography angiography (CTA). In particular, the arterial stiffness is computed linking the aortic kinematic information derived from CTA with pressure waveforms, generated using a lumped parameter model of the arterial circulation. The proposed method is able to include the uncertainty of the input variables as well as to use the entire diameter and blood pressure waveforms over the cardiac cycle rather than only their maximum and minimum values. Although the efficiency and accuracy of the proposed method are tested on a single patient-specific case, the proposed approach is powerful and already possesses the ability to evaluate regional changes of stiffness in human aorta using noninvasive data. The final objective of our paper is to support the adoption of techniques such as CTA as a standard tool for diagnosis and treatment planning of aortic diseases.

Many experimental studies concerning nitric oxide (NO) release from endothelium and its vasodilative action are available in the literature, but no analytical description or modeling of these phenomena can be found. On the contrary, a large modeling literature is available concerning the other cardiovascular control mechanisms, such as the myogenic and metabolic control. In order to analytically describe these phenomena, a model of the endothelial control (defined in the Laplace domain and based on experimental data) was implemented and integrated with a lumped-parameter model of the systemic circulation, consisting of large artery segments and peripheral networks. The endothelial regulation model was based on the hypothesis proposed by Kuchan and Frangos, considering that NO release from the endothelium is generated by two parallel paths. The whole model was then applied to study the different vascular constriction or dilation under continuous or pulsatile perfusion, in order to better understand the clinical evidences of a poor organ perfusion in the presence of continuous with respect to pulsatile cardiopulmonary bypass. According to the experimental evidences, the main results obtained from the model revealed a widespread vascular constriction under continuous perfusion with respect to pulsatile. This result remains constant in the presence of different conditions of blood parameters and flow waveform.