Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans

• Type of Material: Article
• Authors: Minliang Liu, Georgia Institute of Technology; Liang Liang, Georgia Institute of Technology; Fatiesa Sulejmani, Georgia Institute of Technology; Xiaoying Lou, Georgia Institute of Technology; Glen Iannucci, Emory University; Edward Chen, Emory University; Bradley Leshnower, Emory University; Wei Sun, Emory University
• Language: English
• Date: 2019-09-10
• Publisher: Nature Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © 2019, The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-019-49438-w
• Title of Journal or Parent Work: Scientific Reports
• Volume: 9
• Issue: 1
• Start Page: 12983
• End Page: 12983
• Grant/Funding Information: ML - AHA predoctoral fellowship 19PRE34430060; American Heart Association (AHA) 18TPA34230083; National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378
• Keywords: RUPTURE; Multidisciplinary Sciences; Science & Technology; WALL; BEHAVIOR; HUMAN ARTERIES; RISK; STRESSES; Science & Technology - Other Topics; SHAPE
• Research Categories: Health Sciences, General; Engineering, Biomedical

Abstract

Accurate identification of in vivo nonlinear, anisotropic mechanical properties of the aortic wall of individual patients remains to be one of the critical challenges in the field of cardiovascular biomechanics. Since only the physiologically loaded states of the aorta are given from in vivo clinical images, inverse approaches, which take into account of the unloaded configuration, are needed for in vivo material parameter identification. Existing inverse methods are computationally expensive, which take days to weeks to complete for a single patient, inhibiting fast feedback for clinicians. Moreover, the current inverse methods have only been evaluated using synthetic data. In this study, we improved our recently developed multi-resolution direct search (MRDS) approach and the computation time cost was reduced to 1~2 hours. Using the improved MRDS approach, we estimated in vivo aortic tissue elastic properties of two ascending thoracic aortic aneurysm (ATAA) patients from pre-operative gated CT scans. For comparison, corresponding surgically-resected aortic wall tissue samples were obtained and subjected to planar biaxial tests. Relatively close matches were achieved for the in vivo-identified and ex vivo-fitted stress-stretch responses. It is hoped that further development of this inverse approach can enable an accurate identification of the in vivo material parameters from in vivo image data.