Experimental verification of brain tissue incompressibility using digital image correlation.

For decades, incompressibility has been a major assumption in the mechanical study of brain tissue. This assumption is based on the hydrated nature of the biological tissues and the incompressibility of fluids. In this paper, an experimental validation of this assumption using digital image correlation is presented. Unconfined compression tests, relaxation tests and cyclic tests were performed on cylindrical samples of swine brains at loading rates suitable for neurosurgical applications. Digital image correlation was used to evaluate the evolution of the volume ratio throughout the tests. The preparation of the samples is described and it is demonstrated that it causes no statistically significant change of their mechanical properties. The results indicate that the brain tissue incompressibility assumption is verified.

[1]  M. Coret,et al.  Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure. , 2010, Journal of biomechanics.

[2]  Stéphane Roux,et al.  Experimental investigation of localized phenomena using digital image correlation , 2008 .

[3]  W. F. Ranson,et al.  Applications of digital-image-correlation techniques to experimental mechanics , 1985 .

[4]  G W M Peters,et al.  Towards a reliable characterisation of the mechanical behaviour of brain tissue: The effects of post-mortem time and sample preparation. , 2007, Biorheology.

[5]  G. Franceschini,et al.  THE MECHANICS OF HUMAN BRAIN TISSUE , 2006 .

[6]  R. Lakes Foam Structures with a Negative Poisson's Ratio , 1987, Science.

[7]  F. Velardi,et al.  Anisotropic constitutive equations and experimental tensile behavior of brain tissue , 2006, Biomechanics and modeling in mechanobiology.

[8]  P. Bovendeerd,et al.  Design and numerical implementation of a 3-D non-linear viscoelastic constitutive model for brain tissue during impact. , 2004, Journal of biomechanics.

[9]  M. Prange,et al.  Regional, directional, and age-dependent properties of the brain undergoing large deformation. , 2002, Journal of biomechanical engineering.

[10]  G N Duda,et al.  Digital image correlation: a technique for determining local mechanical conditions within early bone callus. , 2007, Medical engineering & physics.

[11]  K. Chinzei,et al.  Mechanical properties of brain tissue in tension. , 2002, Journal of biomechanics.

[12]  K. Chinzei,et al.  Constitutive modelling of brain tissue: experiment and theory. , 1997, Journal of biomechanics.

[13]  J. Crandall,et al.  Nonlinear viscoelastic effects in oscillatory shear deformation of brain tissue. , 2001, Medical engineering & physics.

[14]  M. Coret,et al.  Methodology to determine failure characteristics of planar soft tissues using a dynamic tensile test. , 2007, Journal of biomechanics.

[15]  P. J. Hoopes,et al.  In vivo modeling of interstitial pressure in the brain under surgical load using finite elements. , 2000, Journal of biomechanical engineering.

[16]  J S H M Wismans,et al.  Optical characterization of acceleration-induced strain fields in inhomogeneous brain slices. , 2009, Medical engineering & physics.

[17]  K. Miller,et al.  Constitutive model of brain tissue suitable for finite element analysis of surgical procedures. , 1999, Journal of biomechanics.

[18]  H. Park,et al.  Use of a digital image correlation technique for measuring the material properties of beetle wing , 2009 .

[19]  R. Grebe,et al.  Study of brain white matter anisotropy , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Jaydev P. Desai,et al.  Estimating zero-strain states of very soft tissue under gravity loading using digital image correlation , 2010, Medical Image Anal..

[21]  Karol Miller,et al.  Brain mechanics For neurosurgery: modeling issues , 2002, Biomechanics and modeling in mechanobiology.

[22]  Daniel Scharstein,et al.  View Synthesis Using Stereo Vision , 2001, Lecture Notes in Computer Science.

[23]  K. Miller,et al.  On the unimportance of constitutive models in computing brain deformation for image-guided surgery , 2009, Biomechanics and modeling in mechanobiology.

[24]  S. Roux,et al.  “Finite-Element” Displacement Fields Analysis from Digital Images: Application to Portevin–Le Châtelier Bands , 2006 .

[25]  A. Pitsillides,et al.  Using digital image correlation to determine bone surface strains during loading and after adaptation of the mouse tibia. , 2010, Journal of biomechanics.

[26]  B. Cyganek An Introduction to 3D Computer Vision Techniques and Algorithms , 2009 .

[27]  J. Crandall,et al.  Non-contact strain measurement of biological tissue. , 2004, Biomedical sciences instrumentation.

[28]  D. Arola,et al.  Applications of digital image correlation to biological tissues. , 2004, Journal of biomedical optics.