Computational Fluid Dynamics (CFD) have been in the focus of the biomedical com- munity for many years. In particular, blood flow simulations in the aorta are now well established among researchers. Despite these developments, numerical tools do not consti- tute every day tools in the bedside. The aim of this thesis is to fill the methodological and conceptual gaps between CFD and medical needs in the field of thoracic aortic disease. In Chapter 2, pathologies of the aorta will be described in deep to understand which are the current medical needs. Subsequently, CFD in the aorta will be explained with all the ingredients needed to make robust and simple simulations. Chapter 3 focuses on tuning boundary conditions, a topic where much work has been done in the research community but there is no actual consesnsus. We propose a novel and simple, yet cheap and reliable, methodology for calibrating the so-called 3 Element Windkessel model. Three applications of CFD in the aorta will be shown in Chapter 4 as a proof-of-concept on moving from a single case to bigger cohorts. These are the iCardioCloud project, a database of CFD on patients with thoracic aortic disease; the impact of the aging aorta on hemodynamics and the predictive value of comptational simulations for embodeviation in TAVI. More advanced applications of computational simulations suited for TEVAR will be ap- proached in Chapter 5. First, CFD will be applied in healthy aortas in order to evaluate the impact aortic arch angulation on blood flow for a better TEVAR planning. Then, a frame- work for merging virtual stent-graft deployment together with pre-operative imaging will be proposed so as to reliably predict hemodynamics after TEVAR. Finally, virtual endograft- ing of the ascending aorta will be analyzed as an alternative to open repair. We conclude that the developed methods and tools satisfactorily fill the different gaps needed for simulating big cohorts of patients and extract both single-patient and population- based results.

Moving Computational Tools for Aortic Disease from the Bench to the Bedside

ROMAROWSKI, RODRIGO MAXIMILIANO
2018-03-12

Abstract

Computational Fluid Dynamics (CFD) have been in the focus of the biomedical com- munity for many years. In particular, blood flow simulations in the aorta are now well established among researchers. Despite these developments, numerical tools do not consti- tute every day tools in the bedside. The aim of this thesis is to fill the methodological and conceptual gaps between CFD and medical needs in the field of thoracic aortic disease. In Chapter 2, pathologies of the aorta will be described in deep to understand which are the current medical needs. Subsequently, CFD in the aorta will be explained with all the ingredients needed to make robust and simple simulations. Chapter 3 focuses on tuning boundary conditions, a topic where much work has been done in the research community but there is no actual consesnsus. We propose a novel and simple, yet cheap and reliable, methodology for calibrating the so-called 3 Element Windkessel model. Three applications of CFD in the aorta will be shown in Chapter 4 as a proof-of-concept on moving from a single case to bigger cohorts. These are the iCardioCloud project, a database of CFD on patients with thoracic aortic disease; the impact of the aging aorta on hemodynamics and the predictive value of comptational simulations for embodeviation in TAVI. More advanced applications of computational simulations suited for TEVAR will be ap- proached in Chapter 5. First, CFD will be applied in healthy aortas in order to evaluate the impact aortic arch angulation on blood flow for a better TEVAR planning. Then, a frame- work for merging virtual stent-graft deployment together with pre-operative imaging will be proposed so as to reliably predict hemodynamics after TEVAR. Finally, virtual endograft- ing of the ascending aorta will be analyzed as an alternative to open repair. We conclude that the developed methods and tools satisfactorily fill the different gaps needed for simulating big cohorts of patients and extract both single-patient and population- based results.
12-mar-2018
File in questo prodotto:
File Dimensione Formato  
Romarowski_Tesi.pdf

accesso aperto

Descrizione: tesi di dottorato
Dimensione 37.16 MB
Formato Adobe PDF
37.16 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1227787
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact