A novel approach to the quantification of aortic root in vivo structural mechanics. (28th December 2016) - Votta, E.; Presicce, M.; Della Corte, A.; Dellegrottaglie, S.; Bancone, C.; Sturla, F.; Redaelli, A.
Abstract: Understanding aortic root in vivo biomechanics can help in elucidating key mechanisms involved in aortic root pathologies and in the outcome of their surgical treatment. Numerical models can provide useful quantitative information. For this to be reliable, detailed aortic root anatomy should be captured. Also, since the aortic root is never unloaded throughout the cardiac cycle, the modeled geometry should be consistent with the in vivo loads acting on it. Achieving such consistency is still a challenge, which was tackled only by few numerical studies. Here we propose and describe in detail a new approach to the finite element modeling of aortic root in vivo structural mechanics. Our approach exploits the anatomical information yielded by magnetic resonance imaging by reconstructing the 3‐dimensional end‐diastolic geometry of the aortic root and makes the reconstructed geometry consistent with end‐diastolic loading conditions through the estimation of the corresponding prestresses field. We implemented our approach through a semiautomated modeling pipeline, and we applied it to quantify aortic root biomechanics in 4 healthy participants. Computed results highlighted that including prestresses into the model allowed for pressurizing the aortic root to the end‐diastolic pressure while matching the image‐based ground truth data. Aortic root dynamics, tissues strains, and stresses computed at relevant time points through the cardiac cycle were consistent with a broad set of data from previous computational and in vivo studies, strongly suggesting the potential of the method. Also, results highlighted the major role played by the anatomy in driving aortic root biomechanics. Abstract : We simulated aortic root (AR) dynamics combining subject‐specific anatomical reconstructions, based on in vivo medical imaging, with a realistic modeling of AR loading conditions. This novel approach aimed for the consistency between AR geometry and pressure loads loading it, and hence at a reliable computation of tissues strains and stresses. The proposed strategy was successfully applied to 4 subject‐specific AR models; simulations highlighted the key role of both geometrical features and tissues prestresses and suggested the potential of our method.