Biomedical Engineering Reference
In-Depth Information
wall, i.e. FSG models, such as the one we presented. Such models provide the foun-
dations to model not only aneurysm evolution but other vascular diseases.
It is envisaged that models of IA and AAA evolution may ultimately lead to pre-
dictive models that have diagnostic application on a patient specific basis. Given that
this will yield very substantial healthcare and economic benefits, there is significant
growth of research in this area. However, whilst models of aneurysm evolution have
gained increasing sophistication over the past decade, many further improvements
are still required. For instance, there is a need to incorporate explicit representations
of vascular cells (endothelial cells, fibroblast cells and smooth muscle cells), their
interactions and the signaling networks (Schmid et al., 2011 ) that link the stim-
uli acting on them to their functionality in physiological, supra-physiological and
pathological conditions. There is also a need for implementation of more sophisti-
cated constitutive models to represent, e.g., the collagen fiber recruitment distribu-
tion (Hill et al., 2012 ) and dispersion and the active and passive response of vas-
cular smooth muscle cells (Murtada et al., 2010 ). Lastly, improved understanding
and modeling of how this complex micro-structure adapts in pathological condi-
tions is needed: the modeling framework needs to be validated and/or calibrated
against physiological data; animal models undoubtedly have a role to play in this
respect (Zeng et al., 2011 ). Such enhancements will offer the potential for patient-
specific predictive models of vascular disease evolution and intervention. They will
benefit patients immensely because the decision on whether to/how to intervene
will be founded upon a robust concentration of knowledge with respect to patient-
specific vascular physiology, biology and biomechanics. Of course, the challenging
and multi-disciplinary nature of such research implies collaborations are essential.
Acknowledgements Paul N. Watton is funded by the Center of Excellence in Personalized
Healthcare (funded by the Wellcome Trust and EPSRC, grant number WT 088877/Z/09/Z). This
support is gratefully acknowledged.
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