Medical Image Analysis
Volume 14, Issue 2 , Pages 138-148, April 2010

Measurement and characterization of soft tissue behavior with surface deformation and force response under large deformations

  • Bummo Ahn
  • ,
  • Jung Kim

      Affiliations

    • Corresponding Author InformationCorresponding author. Tel.: +82 42 869 3231; fax: +82 42 869 3210.

School of Mechanical, Aerospace and Systems Engineering, Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Daejeon 305-701, Republic of Korea

Received 13 October 2008; received in revised form 10 July 2009; accepted 29 October 2009. published online 06 November 2009.

Abstract 

Soft tissue characterization with the inverse finite element method (FEM) optimization algorithm plays an important role in developing a physical model for medical simulations. However, tissue characterization that takes into account comprehensive boundary conditions for large deformations remains a challenge due to computational complexities and a lack of experimental data. In this study, soft tissue experiments on porcine livers were performed to measure the surface deformation and force response of soft tissues resulting from indentation loading depending on various indentation depths and two different tip shapes. Measurements were carried out with a three-dimensional (3D) optical system and a force transducer. Using the surface deformation and force response results, we estimated the maximum radius of influence, which can be utilized to determine the minimal required soft tissue model size for the FEM simulation. Considering the influence of the boundary conditions, the model was designed and integrated into an inverse FEM optimization algorithm to estimate the model parameters. The mechanical behavior of large deformations was characterized with FE modeling via hyperelastic and linear viscoelastic models.

Keywords: Soft tissue characterization, Medical simulation, Surface deformation, Large deformation, Inverse finite element method (FEM) optimization algorithm

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PII: S1361-8415(09)00122-4

doi:10.1016/j.media.2009.10.006

Medical Image Analysis
Volume 14, Issue 2 , Pages 138-148, April 2010