Turner Syndrome (TS) is a chromosomal condition that predisposes affected females to congenital cardiovascular anomalies, including bicuspid aortic valve and aortic coarctation. Our research integrates patient-specific imaging with advanced computational modeling, including fluid–structure interaction (FSI), to predict aortic hemodynamic events in TS patients. FSI enables simultaneous analysis of blood flow dynamics and vessel wall mechanics, offering deeper insights into stress distribution, wall deformation, and flow disturbances. This approach reveals elevated wall shear stress and complex vortical flow patterns in the elongated aortic arch typical of TS, which may contribute to increased risk of aortic dissection. By combining imaging data with FSI simulations, we aim to enhance risk stratification and support personalized clinical decision-making for this vulnerable population.
Main clinical collaborator: Aarhus University Hospital – Dep. of Clinical Medicine
Aortic dissection is a life-threatening condition characterized by the separation of the aortic wall layers, forming a false lumen divided from the true lumen by an intimal septum. Our research investigates the biomechanical properties of this septum using advanced in-vivo imaging techniques and advanced in-silico modelling, including cine MRI and phase-contrast flow analysis, to quantify septal motion, stiffness, and deformation under physiological conditions.
These insights are critical for understanding dissection progression and guiding therapeutic strategies. In cases of chronic dissection, we also explore the role of septectomy – a surgical or endovascular technique that removes or fenestrates the septum to restore uniluminal flow and improve outcomes in patients with malperfusion or failed endovascular repair. By integrating imaging-derived biomechanics with clinical interventions, our work aims to enhance risk prediction and optimize treatment planning for complex aortic pathologies.
Main clinical collaborator: Aarhus University Hospital – Dep. of Clinical Medicine
This research investigates the biomechanics of mucociliary clearance—a vital defense mechanism of the respiratory system—through a novel two-phase fluid–structure interaction (FSI) model. The study simulates realistic mucus transport driven by coordinated ciliary motion, incorporating direct cilia modeling and Carreau non-Newtonian rheology to represent the mucus layer. A new method for prescribing cilia beat patterns enables accurate coupling between cilia dynamics and mucus flow. The simulations reveal how variations in cilia activity and mucus properties affect clearance efficiency, offering valuable insights into the mechanical interplay underlying healthy and diseased airway function. This work contributes to the understanding of respiratory fluid mechanics and supports the development of therapeutic strategies for airway diseases.
Recent advancements in understanding valve diseases have led to more effective techniques for aortic valve repair, notably the annuloplasty procedure. Annuloplasty aims to address aortic valve conditions like regurgitation by minimizing the need for complex interventions, reducing associated risks and patient management challenges.The aim is to provide surgeons with insights into optimal annuloplasty techniques, enhancing patient outcomes by determining appropriate device placement and mechanical specifications.
This project focuses on validating an aortic annuloplasty test-bench flow-loop through computational fluid-structure interaction (FSI) analysis, combining experimental work at CAVE Lab and validation with 4D flow magnetic resonance clinical measurements at the Department of Cardiological Medicine (Aarhus University). An active exchange and collaboration with Politecnico di Milano leverage the computational effort.
Calcific aortic valve disease often necessitates percutaneous heart valve replacement, particularly for high-risk patients. To prevent complications like leaflet thrombosis post-implantation, understanding flow disturbances in aortic root is vital.
This project focuses on creating and validating a reliable computational fluid dynamics (CFD) model of transcatheter aortic valve implantation (TAVI), expanding 2D models to fluid-structure interaction (FSI) models validated with in-vitro data acquired at CAVE Lab. Parametric modeling and magnetic resonance scans (acquired at the Department of Cardiological Medicine of Aarhus University) capture leaflet changes during the cardiac cycle. The subsequent 2D FSI model enables fully transient computational analysis, assessing effects like leaflet calcification and stent presence, enhancing insights into valve dynamics and optimizing percutaneous valve replacement procedures.
Blood microvasculature represents the smallest yet the most extensive portion of our circulatory systems. When compromised due to inflammatory or hypoxic conditions, huge sections of the major organs are highly affected and leading to deadly consequences. In the very last decades, hydrogels have emerged as ultimate resources for the treatment of local injuries given their high biocompatibility, biodegradability, and application flexibility.
This wide project includes creating an integrative platform to engineer the hydrogel-blood cell interaction by coupling biomolecular mechanics and computational fluid dynamics (CFD). By conjugating these two computational and dimensional levels, we aim at defining the most important parameters to describe the hydrogel-based process. The key coefficients of the multiscale model are estimated by performing preliminary experiments with controlled 3D-printed vasculature structures where known commercial hydrogels are introduced and perfused.
