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Proceedings Paper

Constrained hyperelastic parameters reconstruction of PVA (Polyvinyl Alcohol) phantom undergoing large deformation
Author(s): Hatef Mehrabian; Abbas Samani
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Paper Abstract

The nonlinear mechanical behavior of tissues that undergo large deformations, e.g. the breast, is characterized by hyperelastic parameters. These parameters take into account both types of nonlinearities: tissue intrinsic nonlinearity and geometric nonlinearity. Elastography technique capable of tissue hyperelastic parameter reconstruction has important clinical applications such as cancer diagnosis and interventional procedure planning. In this study we report our progress on the development of constrained reconstruction technique of breast tissue hyperelastic parameters [1]. The extension of this work is twofold: the inclusion of the popular Veronda-Westmann hyperelastic model and using a novel technique for tissue displacement tracking. This tracking technique is based on the Horn-Schunck optical flow method [2]. The objective of this paper is to validate the numerical analysis performed in [1] by phantom experiment. For this purpose, a PVA (Polyvinyl Alcohol) phantom that consists of three tissue types was constructed and tested. PVA exhibits nonlinear mechanical behavior and has been recently used for tissue mimicking purposes. Reconstruction results showed that it is feasible to find the relative hyperelastic parameters of the tissue with acceptable accuracy. The error reported for the relative parameter reconstruction was less than 20%, which may be sufficient for cancer diagnosis purposes.

Paper Details

Date Published: 13 March 2009
PDF: 11 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72612G (13 March 2009); doi: 10.1117/12.813871
Show Author Affiliations
Hatef Mehrabian, The Univ. of Western Ontario (Canada)
Abbas Samani, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)

Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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