Patient-specific finite element (FE) modeling is largely used to quantify mitral valve (MV) biomechanics associated to pathological and post-surgical conditions. We used this approach, integrated with non-invasive cardiac magnetic resonance (CMR) imaging data, to numerically perform the repair of the isolated mitral valve leaflet prolapse through expanded-polytetrafluoroethylene (ePTFE) sutures and quantitatively compare the effects of different techniques of neochordal implantation (NCI). CMR-derived FE models well reproduced MVP-related alterations and were able to assess the efficacy of each repairing technique and its biomechanical effects onMVapparatus; the quantification of biomechanical differences between NCI techniques, especially in terms of both chordal tensions and leaflet stresses redistribution, may impact on the short- and long-term the clinical outcome, potentially opening the way to patient-specific optimization of NCIs and, if extensively and successfully tested, improve surgical planning.

Repair of mitral valve prolapse through ePTFE Neochordae: A finite element approach from CMR

Milano A. D.;
2015-01-01

Abstract

Patient-specific finite element (FE) modeling is largely used to quantify mitral valve (MV) biomechanics associated to pathological and post-surgical conditions. We used this approach, integrated with non-invasive cardiac magnetic resonance (CMR) imaging data, to numerically perform the repair of the isolated mitral valve leaflet prolapse through expanded-polytetrafluoroethylene (ePTFE) sutures and quantitatively compare the effects of different techniques of neochordal implantation (NCI). CMR-derived FE models well reproduced MVP-related alterations and were able to assess the efficacy of each repairing technique and its biomechanical effects onMVapparatus; the quantification of biomechanical differences between NCI techniques, especially in terms of both chordal tensions and leaflet stresses redistribution, may impact on the short- and long-term the clinical outcome, potentially opening the way to patient-specific optimization of NCIs and, if extensively and successfully tested, improve surgical planning.
2015
978-3-319-10980-0
978-3-319-10981-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/370791
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