An in vivo parameter identification method for arteries: numerical validation for the human abdominal aorta. Issue 4 (12th March 2019)

• Record Type: Journal Article
• Authors: Gade, Jan-Lucas; Stålhand, Jonas; Thore, Carl-Johan
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 22:Issue 4(2019)
• Page Start: 426
• Page End: 441
• Publication Date: 2019-03-12
• Subjects: In vivo -- parameter identification -- abdominal aorta -- in silico -- finite element method -- validation; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2018.1561878
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: A method for identifying mechanical properties of arterial tissue in vivo is proposed in this paper and it is numerically validated for the human abdominal aorta. Supplied with pressure-radius data, the method determines six parameters representing relevant mechanical properties of an artery. In order to validate the method, 22 finite element arteries are created using published data for the human abdominal aorta. With these in silico abdominal aortas, which serve as mock experiments with exactly known material properties and boundary conditions, pressure-radius data sets are generated and the mechanical properties are identified using the proposed parameter identification method. By comparing the identified and pre-defined parameters, the method is quantitatively validated. For healthy abdominal aortas, the parameters show good agreement for the material constant associated with elastin and the radius of the stress-free state over a large range of values. Slightly larger discrepancies occur for the material constants associated with collagen, and the largest relative difference is obtained for the in situ axial prestretch. For pathological abdominal aortas incorrect parameters are identified, but the identification method reveals the presence of diseased aortas. The numerical validation indicates that the proposed parameter identification method is able to identify adequate parameters for healthy abdominal aortas and reveals pathological aortas from in vivo -like data.