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.