4 Brain Tissue Mechanical Properties
83
Compressive properties have also been considered in the context of brain tissue
being a fluid-saturated two-phase material. The main application of this type
of modelling has been in the study of hydrocephalus. The simpler biphasic or
poroelastic models, similar to those developed for modelling soils, assume a linear
elastic tissue matrix saturated with an inviscid (or alternatively a Newtonian) fluid.
This gives rise to flow through the interstitial spaces of the tissue according to
Darcy’s Law, coupled to linear elastic deformation.
Conducting the traditional soil consolidation tests on brain tissue samples has
been said to be technically challenging [53], and thus unconfined compression
data is often used to estimate the properties. Chinzei and Miller [58, 59] showed
that a simple poroelastic model is not able to simulate the strain-rate sensitivity
observed in brain tissue. That group have also published information on methodological issues with such multiphase models and for specific applications such as
hydrocephalus [58, 60]. Cheng and Bilston [53] used a poroviscoelastic model for
brain tissue to model their compressive data at low loading rates. There is a wide
range of values (approximately 3–4 orders of magnitude reported for the hydraulic
conductivity of brain tissue in the literature (2 × 10 −10 –4 × 10 −7 m/s) [53, 61,
62], of which very few are based on definitive experimental work (e.g. [53]. who
reported 4.0 × 10 −7 m/s), and further research is needed to accurately characterise
these parameters. Chapter 6 contains additional discussion of the application of
multiphase models in surgical simulation.
4.4 Tensile Properties of Brain Tissue
Brain tissue properties in tension are less well characterised than in other loading
modes, with only a few studies reporting tensile properties. This is at least in part
due to the difficulties of conducting these tests, particularly in gripping samples
effectively. General observations of the behaviour of brain tissue in tension are that
it appears to soften with increasing strain and exhibits a strain-rate sensitivity that
is consistent with the response in other loading modes, that is, increasing apparent
stiffness with increasing loading rate. Figure 4.8 shows some of the data from the
literature [63, 64]. At higher loading rates, Rashid et al. [65] also observed strong
rate dependence in ex vivo porcine brain specimens, with stresses approximately
doubling for a given strain between 30/s and 90/s strain rates. Failure limits in
tension are not well characterised, but appear to be in the range of 20–60% strain.
More recently, Schiavone et al. [66] have used an aspiration method to measure
in vivo brain deformation with tensile loading at the surface intra-operatively on a
human patient. They used a simplified finite element model to estimate hyperelastic
parameters for that patient.
83
Compressive properties have also been considered in the context of brain tissue
being a fluid-saturated two-phase material. The main application of this type
of modelling has been in the study of hydrocephalus. The simpler biphasic or
poroelastic models, similar to those developed for modelling soils, assume a linear
elastic tissue matrix saturated with an inviscid (or alternatively a Newtonian) fluid.
This gives rise to flow through the interstitial spaces of the tissue according to
Darcy’s Law, coupled to linear elastic deformation.
Conducting the traditional soil consolidation tests on brain tissue samples has
been said to be technically challenging [53], and thus unconfined compression
data is often used to estimate the properties. Chinzei and Miller [58, 59] showed
that a simple poroelastic model is not able to simulate the strain-rate sensitivity
observed in brain tissue. That group have also published information on methodological issues with such multiphase models and for specific applications such as
hydrocephalus [58, 60]. Cheng and Bilston [53] used a poroviscoelastic model for
brain tissue to model their compressive data at low loading rates. There is a wide
range of values (approximately 3–4 orders of magnitude reported for the hydraulic
conductivity of brain tissue in the literature (2 × 10 −10 –4 × 10 −7 m/s) [53, 61,
62], of which very few are based on definitive experimental work (e.g. [53]. who
reported 4.0 × 10 −7 m/s), and further research is needed to accurately characterise
these parameters. Chapter 6 contains additional discussion of the application of
multiphase models in surgical simulation.
4.4 Tensile Properties of Brain Tissue
Brain tissue properties in tension are less well characterised than in other loading
modes, with only a few studies reporting tensile properties. This is at least in part
due to the difficulties of conducting these tests, particularly in gripping samples
effectively. General observations of the behaviour of brain tissue in tension are that
it appears to soften with increasing strain and exhibits a strain-rate sensitivity that
is consistent with the response in other loading modes, that is, increasing apparent
stiffness with increasing loading rate. Figure 4.8 shows some of the data from the
literature [63, 64]. At higher loading rates, Rashid et al. [65] also observed strong
rate dependence in ex vivo porcine brain specimens, with stresses approximately
doubling for a given strain between 30/s and 90/s strain rates. Failure limits in
tension are not well characterised, but appear to be in the range of 20–60% strain.
More recently, Schiavone et al. [66] have used an aspiration method to measure
in vivo brain deformation with tensile loading at the surface intra-operatively on a
human patient. They used a simplified finite element model to estimate hyperelastic
parameters for that patient.