Inverse estimation of fibre reinforced soft tissue of human gallbladder wall

Luo, X. , Li, W., Hill, N. , Ogden, R. and Smythe, A. (2011) Inverse estimation of fibre reinforced soft tissue of human gallbladder wall. In: Inverse Problems in Science and Engineering, Cambridge, UK, 12-16 Dec 2011,

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Publisher's URL: http://sms.cam.ac.uk/media/1197114

Abstract

Cholecystectomy (surgical removal of the gallbladder) for gallbladder pain is the most common elective abdominal operation performed in the western world. However, the outcome is not entirely satisfactory as the mechanism of gallbladder pain is unclear. We have developed a mechanical model of gallbladder aiming to understand its mechanical behaviour. To apply this model to clinical situations, it is often necessary to estimate the material properties from non-invasive medical images. In this work, we present a non gradient-based optimization inverse approach for estimating the elastic modulus of human gallbladders from ultrasound images. Two forward problems are considered. One utilizes a linear orthotropic material model and tracks the Elastic moduli in the circumferential and longitudinal directions. The other is a nonlinear Holzapfel-Grass-Ogden model in which two families of fibres are embedded circumferentially in an otherwise homogeneous Neo-Hookean elastin matri x. These forward problems are solved using the finite element package Abaqus, and a python/Matlab based optimization algorithm is developed to search the global minimum of the error functional, which measures the difference in geometries from the numerical predictions and images. We will compare and analyse the results for six gallbladder samples, and discuss the outstanding challenging issues.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ogden, Professor Raymond and Luo, Professor Xiaoyu and Hill, Professor Nicholas and Li, Dr Wenguang
Authors: Luo, X., Li, W., Hill, N., Ogden, R., and Smythe, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics

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