 An Iterative Hyperelastic Parameters Reconstruction for Breast Cancer Assessment  Hatef Mehrabian 1 , Abbas Samani 1,2,3 1 Department of Electrical & Computer Engineering, University of Western Ontario, London, ON, Canada 2 Department of Medical Biophysics, University of Western Ontario, London, ON, Canada 3 Imaging Research Laboratories, Robarts Research Institute, London, ON, Canada ABSTRACT In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue. KEYWORDS: Breast Cancer, Elastography, Hyperelactic, Inverse Problem, Regularization 1. INTRODUCTION Elastography is a non-invasive method in which stiffness or strain images of soft tissues are used to detect or classify tumors. It is known that changes in the stiffness of soft tissues are associated with the presence of pathology. In breast cancer, a tumor or a suspicious cancerous growth is normally stiffer than the background normal soft tissue. This forms the basis for the commonly used breast manual palpation technique initially used for breast cancer detection. Manual palpation; however, is not sufficiently sensitive with cases where the tumor is not large enough or is located deep within the breast. In such cases, the tumor cannot be detected by palpation in early stages [1]. Thus more qualitative methods are required to detect the presence of abnormalities. In classic elastography the tissue is assumed to exhibit linear behavior, and using Hooke’s law, the tissue's elastic behavior is characterized with only one parameter known as the Young’s modulus. In quasi-static elastography, the tissue is stimulated by applying very low frequency external compression. While linear elastic behavior of tissue is expected when very small external compression is applied, soft tissues especially breast tissues deform significantly as a result of even small and inevitable body motions such as chest motion due to respiration. As such, applying small external compressions to ensure linear elastic behavior is problematic due to the presence of other uncontrollable factors, e.g. body motion, which lead to tissue compression comparable to the one resulting from external stimulation. In other words, linear elastic behavior is maintained at the cost of having small signal-to-noise ratio (SNR) of tissue deformation. To address this problem, large external compression is applied to the tissue, which results in large tissue deformation. Furthermore, in breast quasi-static elastography, tissue deformation can be very large due to low stiffness of the tissue and lack of physical constraints. This results in nonlinear behavior of the tissue. At large deformations most tissues exhibit significant strain hardening and the Young’s modulus can no longer be considered constant [2-3]. As such linear elasticity is not sufficient to model the tissue deformation, thus hyperelastic models are used in this case. Ignoring Hyperelastic effects generally leads to sub-optimal contrast (stiffer tissues at lower strain are contrasted against softer tissues at higher strain) [4]. Medical Imaging 2008: Physiology, Function, and Structure from Medical Images edited by Xiaoping P. Hu, Anne V. Clough, Proc. of SPIE Vol. 6916, 69161C, (2008) 1605-7422/08/$18 · doi: 10.1117/12.770971 Proc. of SPIE Vol. 6916 69161C-1