Computational simulations represent a powerful tool for the pre-procedural clinical assessment of minimally
invasive cardiovascular interventions [1]. Patient-specific simulations rely on the accurate numerical
implementation of both geometrical and mechanical features. While current imaging techniques
are able to depict accurately patient-specific anatomies, at date, a similar image-based tool capable to
retrieve subject-specific material properties is missing.
The scope of this study is to present a framework, involving in silico tools and 3D printing, for the
refinement of an image-based technique [2] capable to retrieve in vivo patient-specific mechanical information
from functional and morphological magnetic resonance imaging (MRI) data. The workflow consists
in three main steps: (i) selection and mechanical testing of 3D commercially available deformable
3D printed materials; (ii) fluid-structure interaction (FSI) simulation of a vessel model under pulsatile
regime; (iii) 3D printing of the model and experimental replica in MRI environment. Finally, the imagebased
technique is applied to both numerical (ii) and MRI data (iii) to retrieve material information to
compare to reference (i).
The described workflow strategy was successfully implemented by our group. The deformable material
TangBlackPlus FLX980 was selected and mechanically tested, resulting in an elastic module (E) of
0.50 0:02 MPa. A vessel model was designed for FSI simulations (E=0.50 MPa) as well as 3D printed
with an Objet500 Connex machine (Stratasys, Minnesota, USA) to acquire MRI data. The image-based
technique was used to retrieve the E value from numerical and experimental data. In silico, the indirect
material evaluation resulted in E=0.49 MPa, while in vitro we found E=0.51 0:04 MPa. Moreover, other
values of E (up to 32 MPa) were tested in silico, leading to matching results as well. Other deformable
materials will be investigated, i.e. Agilus30 (Stratasys, Minnesota, USA) and Elastic and Flexible resins
(Formlabs, Massachusetts, USA), by using the described framework. With further refinements, this strategy
would lead to an indirect and image-based tool for the in vivo assessment of patient-specific material
properties, thus enhancing the confidence of patient-specific computational models.
