RESEARCH
1. Map
quantitatively and non-invasively, with a microscopic resolution, the local
mechanical properties (stiffness, resistance, tensile and shear stresses) in
the extracellular matrix of biological tissues.
In my team, it has become a common
practice to combine image based full-field displacement measurements
experienced by tissue samples in vitro, with custom inverse methods to infer
(using nonlinear regression) the best-fit material parameters and the rupture
stresses and strains. We also use similar approaches for characterizing the
material parameters of soft tissues in vivo, where advanced medical imaging can
provide precise measurements of tissue deformation under different modes of
action, and inverse methodologies are used to derive material properties from
those data. These approaches offer important possibilities for fundamental mechanobiology which aims at gaining better insight in the
growth, remodeling and ageing effects in biological
tissues. It is well-known that biological soft tissues appear to develop, grow,
remodel, and adapt so as to maintain particular mechanical metrics (e.g.,
stress) near target values. To accomplish this, tissues develop regionally
varying stiffness, strength and anisotropy. Important challenges in soft tissue
mechanics are now to develop and implement hybrid experimental - computational
method to quantify regional variations in properties in situ.
The main motivation of my research is to contribute to this
field by developing the virtual fields method (VFM).
We have achieved characterizations
combining biaxial extension–distension testing, optical coherence tomography
(OCT), digital volume correlation (DVC) and the VFM. Taking advantage of this
unique combination of techniques, we are now relating for the first time mechanical
alterations to genetic modifications.
[1]
Mei, Y., Liu, J., Guo, X., Zimmerman, B., Nguyen, T.D. & Avril, S. General finite-element
framework of the Virtual Fields Method in Nonlinear Elasticity. Journal of
Elasticity, in press, 2021.
[2]. Mei, Y., & Avril, S. On improving the accuracy of nonhomogeneous shear modulus
identification in incompressible elasticity using the virtual fields method. International Journal of Solids and
Structures, 2019, 178, 136-144.
[3] Bersi, M. R., Santamaría,
V. A. A., Marback, K., Di Achille,
P., Phillips, E. H., Goergen, C. J., Humphrey, J.D., Avril, S. Multimodality imaging-Based characterization of Regional
Material properties in a Murine Model of Aortic Dissection. Scientific Reports,
2020, 10(1), 1-23.
2. Computer modelling for assisting cardiovascular surgical
interventions
The surgical decision to treat an
aortic aneurysm to prevent rupture is based on its maximum external diameter.
However, placing and assessing the largest aneurysm diameter is a complex and
time-consuming task on a distorted 3D anatomy, requiring multiplanar
reconstructions. It leads to significant inter- and intra-reader variability.
Various digital tools have already been commercially introduced to facilitate
aorta external diameter measurement while reducing inter- and intra-reader variability,
but none offering the ability to automatically segment the entire aorta
including the thrombus and provide maximum outer to outer wall diameter
perpendicular to the central axis.
We have developed reduced order model
and machine learning techniques, permitting very fast computational analyses
for computer assisted EVAR. This solution, which relies on virtual reality, is
based on a single intraoperative X-ray image.
In 2017, I co-founded Predisurge, a spin-off company of IMT at Mines Saint-Etienne.
PrediSurge offers innovative software solutions for
patient-specific numerical simulation of surgical procedures. Clinical studies
have validated PrediSurge’s digital twin technology,
which provides an exceptional 99% precision in SG fenestration positioning as
compared to standards. With digitalization, the SG sizing process is radically
shortened, from 1 month to 1 hour. PrediSurge’s
technology has already been incorporated in the planning process of one major
stent manufacturer and is used for over 100 patients from eleven clinical centers in 5 European countries.
[1] L Derycke,
D Perrin, F Cochennec, JN Albertini, S.
Avril, Predictive numerical simulations of double branch stent-graft
deployment in an aortic arch aneurysm, Annals of Biomedical Engineering, 2019,
47(4), 1051-1062.
[2]. Derycke,
L., Sénémaud, J., Perrin, D., Avril, S., Desgranges, P., Albertini, J.
N., & Haulon, S. Patient Specific Computer
Modelling for Automated Sizing of Fenestrated Stent Grafts. European Journal of
Vascular and Endovascular Surgery, 2020, 59(2), 237-246.
[3]. Avril,
S. Future directions for personalized computer simulations in endovascular
aneurysms repair. International Journal of Cardiology (editorial), 2020.
3. Computer modelling in mechanobiology of aortic aneurysms
Rupture of Aortic Aneurysms (AA) kills more than 30000 persons every
year in Europe and the USA. It is a complex phenomenon that occurs
when the wall stress exceeds the local strength of the aorta due to degraded
properties of the tissue. The state of the art in AA biomechanics and mechanobiology revealed in 2014 that major scientific
challenges still had to be addressed to permit patient-specific
computational predictions of AA rupture and enable localized repair of the
structure with targeted pharmacologic treatment. A first challenge related to
ensuring an objective prediction of localized mechanisms preceding rupture. A
second challenge related to modelling the patient-specific evolutions of
material properties leading to the localized mechanisms preceding rupture. We
worked at addressing these challenges in my ERC grant entitled BIOLOCHANICS (2015-2020).
We developed a digital twin framework for helping clinicians to
establish prognosis for patients harbouring an AA. Thanks to Magnetic Resonance
Imaging and computer fluid dynamics simulations, we estimate hemodynamics loads on the aortic wall. The impact of
the hemodynamics loads on the mechanical
properties aortic tissue and aortic smooth muscle cells has been
extensively characterized throughout the project. Eventually we simulate
the induced evolutions through finite element models to predict aneurysmal
progression and potential risk of rupture.
My team was the first to carry out patient-specific finite element modeling taking into account the mechanical interactions
between fluids and tissues as well as the biological processes involved in the remodeling of structural proteins (collagen, elastin) and
cells.
[1] W Krasny,
H Magoariec, C Morin, S Avril, A
comprehensive study of layer-specific morphological changes in the
microstructure of an arterial wall under uniaxial load, Acta
Biomaterialia, 2017, 57, 342-351.
[2] Condemi, F., Campisi,
S., Viallon, M., Croisille, P., & Avril,
S. Relationship between ascending thoracic aortic aneurysms hemodynamics
and biomechanical properties. IEEE Transactions on Biomedical Engineering, 2019
[3].
Petit, C., Karkhaneh, A., Michel, J.B., Guignandon, A., & Avril, S. Regulation of SMC traction
forces in human aortic thoracic aneurysms. Biomechanics and Modeling in
Mechanobiology, 2021, 1-15.
[4] Santamaría,
V. A. A., García, M. F., Molimard, J., & Avril, S. Characterization of Chemoelastic Effects in Arteries Using Digital Volume
Correlation and Optical Coherence Tomography. Acta Biomaterialia, 2020
[5].
Mousavi, J., Jayendiran, R., & Avril,
S. Coupling hemodynamics with mechanobiology
in patient-specific computational models of ascending thoracic aortic
aneurysms. Computer
Methods and Programs in Biomedicine, in press, 2021.