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Simona Celi

Researcher at Sant'Anna School of Advanced Studies

Publications -  71
Citations -  644

Simona Celi is an academic researcher from Sant'Anna School of Advanced Studies. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 9, co-authored 41 publications receiving 343 citations.

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Impact of uncertainties in outflow boundary conditions on the predictions of hemodynamic simulations of ascending thoracic aortic aneurysms

TL;DR: In this paper, the impact of uncertainties in the Windkessel model parameters on the results of numerical simulations of the flow inside ascending thoracic aortic aneurysms is investigated.
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Validation of Numerical Simulations of Thoracic Aorta Hemodynamics: Comparison with In Vivo Measurements and Stochastic Sensitivity Analysis

TL;DR: A successful integration of hemodynamic simulations and of MRI data for a patient-specific simulation has been shown and the wall compliance seems to have a significant impact on the numerical predictions; a larger wall elasticity generally improves the agreement with experimental data.
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A 3D printed melt-compounded antibiotic loaded thermoplastic polyurethane heart valve ring design: an integrated framework of experimental material tests and numerical simulations

TL;DR: In this article, the authors discuss valve pathologies such as valve stenosis, regurgitation, failure and similar, for which usually a valve substitution is performed. But valve substitution may not always be beneficial.
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Computational fluid dynamic study for aTAA hemodynamics: an integrated image-based and RBF mesh morphing approach.

TL;DR: A novel framework for the fluid dynamics analysis of healthy subjects and patients affected by ascending thoracic aorta aneurysm (aTAA) and the proposed integrated approach of RBF morphing technique and CFD simulation for aTAA was demonstrated.
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In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading.

TL;DR: A new framework is presented that allows a segmentation and quantification of OCT images of coronary arteries to define the plaque type and stenosis grading and shows that automated segmentation of the vessel and of the tissue components are possible off-line with a precision that is comparable to manual segmentation for the tissue component.