March 2023 – MeDiTATe project
Monthly Archives: March 2023
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31 March 2023

An interview with Dr. Simona Celi, Ethic Coordinator of the MeDiTATe project

We had a small talk with Dr. Simona Celi, a member of the Board of the MeDiTATe project. She is a Senior scientist at Fondazione Toscana G. Monasterio (FTGM) and co-founder of the BioCardioLab. In her experience, she participated to several National and International research projects and currently she is the technical Project Investigator (PI) and sub-unit PI of three national research grants. During the project, she will be supervising ESR09, ESR10 and ESR14.

The MeDiTATe project, a research proposal funded by the European Commission in the framework of Horizon 2020 programme. Can you tell us about FTGM and its role in the project? Read More

23 March 2023

The MeDiTATe Winter School “Big Data Analytics, from engineering to clinical”

The Fondazione Toscana G. Monasterio hosted the MeDiTATe project Winter School “Big Data Analytics, from engineering to clinical”. The event took place from 20th to 24th February 2023 at Pisa, Italy.

The Early Stage Researchers of the MeDiTATe project had the opportunity to follow several courses regarding both the clinical and genetic view of aneurysms and the application of AI and big data to hemodynamics studies.

The Winter School was also an opportunity to strengthen the collaboration among the Early-Stage Researchers and to present their own results.

10 March 2023

“Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls”, a new publication in collaboration with the MeDiTATe project

A new paper Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls has been published in the MDPI Journal of Cardiovascular Development and Disease in collaboration with the MeDiTATe project.

The paper was written by Benigno Marco Fanni (BioCardioLab FTGM), Maria Nicole Antonuccio, ESR 14 of the MeDiTATe project, Simona Celi (BioCardioLab FTGM), Alessandra Pizzuto and Giuseppe Santoro (Pediatric Cardiology Unit, FTGM), Sergio Berti (Adult Cardiology Unit, FTGM).

Here follows the paper’s abstract:

Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. Results: The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.

6 March 2023

A new MeDiTATe project publication: “Assessment of shape-based features ability to predict the ascending aortic aneurysm growth”

Leonardo Geronzi, ESR 02 of the MeDiTATe project, published the paper Assessment of shape-based features ability to predict the ascending aortic aneurysm growth in the Frontiers in Physiology journal.

This work involved the collaboration of several universities, research centres and three French hospitals: the University Hospital of Rennes, the University Hospital of Dijon and the University Hospital of Toulouse.

The full paper is available at this link on the Frontiers journal website. Antonio Martinez Pascual, ESR 01 of the MeDiTATe project, also participated in this work.

This publication shows the importance of considering the shape of the ascending aorta in predicting the risk of aneurysm growth. In fact, the work aims at explaining how to compute some shape features on the ascending tract and how to use machine learning classifiers to assess the risk of growth. The results provide a clear indication that a set of shape features could be selected to identify patients at high risk of growth more accurately than the maximum diameter alone.

Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 859836
Email: meditate@uniroma2.eu
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