AEM Seminar Series: Dr. Sevan Goenezan
Dr. Sevan Goenezan, an Associate Professor of Mechanical Engineering and Applied Mathematics and Computational Sciences and Robert and Virginia Wheeler Faculty Fellow of Engineering at the University of Iowa, will be speaking as part of the Aerospace Engineering and Mechanic’s Research Seminar Series on “Electro-Magneto-Mechanical Coupling in Anisotropic Electrically Conductive Composites”.
The elastic properties of soft tissues can be utilized to distinguish diseased tissues from normal, such as breast tumors, liver cirrhosis, prostate cancer etc. The elastic property distribution of the tissue region of interest is determined by solving an inverse problem in elasticity. In general, this requires the knowledge of the displacement field, which can be determined from imaging modalities (e.g., ultrasound and magnetic resonance imaging) and well developed algorithms (e.g. cross-correlation and block matching algorithms). The inverse problem is posed as a constrained minimization problem, where the discrepancy between a computed and measured displacement field is minimized under a Tikhonov regularization. The computed displacement field satisfies the equations of equilibrium, which is solved using finite element techniques. The solution of the inverse problem has proven to be robust in the presence of noisy displacement data when the problem domain contains one inclusion [1, 2]. Recently, we observed that the solution of the inverse problem utilizing two inclusions appears to be sensitive to the choice of boundary conditions . In other words, the elastic property reconstruction changes significantly when boundary conditions are altered, e.g. from uniform displacement compression to linear displacement compression.
In this presentation, we will analytically show that the solution of the inverse problem depends on the strain field. We address this issue, utilizing a new formulation of the minimization function and test it on three benchmark tests. Furthermore, we increase the overall contrast of the inclusions drastically by utilizing a modified version of the Tikhonov regularization. In the second part of this talk, we will present the solution of the inverse problem utilizing only surface displacement data. We will show that the solution of the inverse problem with measured displacement data from the specimen’s surface, yields robust reconstructions of the elastic property distribution.
1. Goenezen, S., P. Barbone, and A.A. Oberai, Solution of the nonlinear elasticity imaging inverse problem: The incompressible case. Computer
Methods in Applied Mechanics and Engineering, 2011. 200(13-16): p. 1406-1420.
2. Goenezen, S., J.F. Dord, Z. Sink, P. Barbone, J. Jiang, T.J. Hall, and A.A. Oberai, Linear and nonlinear elastic modulus imaging: An application to breast
cancer diagnosis. IEEE Trans Med Imaging, 2012. 31(8): p. 1628-37.
3. Mei, Y. and S. Goenezen, Spatially weighted objective function to solve the inverse problem in elasticity for the elastic property distribution. to
appear in Computational Biomechanics for Medicine IX, 2014.
Dr. Sevan Goenezen received his BS/MS degree (called Diplom in Germany) in Aeronautical Engineering from the “Rheinisch-Westfaelische Technische Hochschule Aachen (RWTH)” in Germany in December 2006. He started his
Ph.D. program in spring 2007 at Rensselaer Polytechnic Institute (RPI) and graduated in May 2011. His Ph.D. work involved the development and implementation of efficient algorithms to solve inverse problems in finite elasticity. Its application to nonlinear elasticity imaging of breast tumors has shown great potential to diagnose breast cancer non-invasively. He became a finalist for the $30,000 Lemelson-MIT-RPI Prize with his findings on breast cancer diagnosis using nonlinear elasticity imaging. After his PhD, Sevan Goenezen became a postdoctoral researcher in the Biomedical Engineering Department at Oregon Health & Science University, where he studied congenital heart defects of the embryonic chicken heart and risk of rupture of abdominal aortic aneurysms using fluid-structureinteraction computations. He recently joined the Department of Mechanical Engineering at Texas A&M University as an Assistant Professor. His research interests are in computational biomechanics, growth and remodeling of tissues, inverse problems, fluid-structure-interactions, and multi-scale modeling. During his time at Texas A&M University, he was awarded the ASME Research Initiation Grant for young faculty sponsored by the Haythornthwaite Foundation and the Southeastern Conference (SEC) Visiting Faculty Travel Grant.