Fanni, BM; Sauvage, E; Capelli, C; Gasparotti, E; Vignali, E; Schievano, S; Landini, L; ... Celi, S; + view all Fanni, BM; Sauvage, E; Capelli, C; Gasparotti, E; Vignali, E; Schievano, S; Landini, L; Positano, V; Celi, S; - view fewer (2019) A Numerical and 3D Printing Framework for the in Vivo Mechanical Assessment of Patient-specific Cardiovascular Structures. In: Auricchio, F and Rank, E and Steinmann, P and Kollmannsberger, S and Morganti, S, (eds.) Second International Conference On Simulation For Additive Manufacturing (SIM-AM 2019). (pp. pp. 31-39). International Centre for Numerical Methods in Engineering (CIMNE): Pavia, Italy.

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Abstract

Computational simulations represent a powerful tool for the pre-procedural clinical assessment of minimally invasive cardiovascular interventions. 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 capable to retrieve in vivo patient-specific mechanical information from functional and morphological magnetic resonance imaging (MRI) data. The workflow consists in different 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) elaboration of in silico results and calibration of the image-based method; (iv) 3D printing of the model and experimental replica in MRI environment; (v) finally, the image-based technique is applied to MRI data (iv) to retrieve material information to compare to reference (i). The described workflow strategy was successfully implemented by our group. The deformable material TangBlackPlus FLX980 (TangoPlus) was selected and mechanically tested, resulting in an elastic module (E) of 0.50±0.02 MPa (n = 5). FSI simulations of a simplified vessel were carried out with different E values (from 0.5 to 32 MPa). In silico, the indirect material evaluation resulted, after the calibration, in a good matching between inputted E values and estimated ones, leading to a percentage difference of 7.8±4.1% (n = 12). The simulated vessel was 3D printed with TangoPlus and acquired in terms of MRI data. The application of the proposed image-based method resulted in a E value of the phantom of E = 0.54 MPa, very close to the one directly assessed via tensile tests (0.5 MPa). Although very good results were achieved in this study, other deformable materials and shapes will be investigated 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.

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