Biomedical Engineering Reference
In-Depth Information
Chapter 26
Mechanical Characterization of the Human
Liver
Marc Hollenstein and Edoardo Mazza
Abstract
The aspiration technique was used to characterize the mechanical behav-
ior of the liver. Intra-operative application on human organs aimed at (i) tissue clas-
sification towards development of novel diagnostic procedures, and (ii) constitu-
tive modeling of liver tissue. The first goal was achieved using scalar parameters
extracted from time histories of aspiration pressure and deformation. Determina-
tion of parameters for nonlinear time dependent constitutive model formulations
required solving the inverse problem. Glisson's capsule was analyzed separately
from parenchyma and was shown to behave as
I
2
-material. 207 aspiration measure-
ments were performed on 33 patients. The influence of the contact force between
the aspiration device and the liver was kept minimal in order to achieve a high re-
producibility of the mechanical measurements. Histopathological characterization
with biopsies taken at the measurement location allowed analyzing the influence of
tissue microstructure. Tumors with high connective tissue content were shown to
significantly affect the mechanical response.
26.1 Introduction
Simulation based planning and virtual reality training of minimally invasive hep-
atic surgery requires realistic constitutive models formulation and corresponding
parameters (Schwartz et al.,
2005
). Recent advances in diagnostic procedures rely
on the characterization of hepatic tissue's mechanical response, such as in dynamic
magnetic resonance elastography for the detection of liver fibrosis (Huwart et al.,
2006
; Rouvière et al.,
2006
). Knowledge of the mechanical behavior of human liver
might also contribute to improvements in radiation therapy. In fact, 4D CT or MRI
is used for evaluation of organ displacement due to respiratory motion (von Sieben-
thal et al.,
2007
) and realistic mechanical models of liver tissue could improve the
predictive capabilities of the algorithms used for tumor localization.
M. Hollenstein
·
E. Mazza (
