Conference Agenda

Introduction

Biomechanical stimuli are crucial in the progression of ascending thoracic aortic aneurysm (ataa) [1,2,3]. Currently, biomechanical stimuli are typically obtained in silico through computational models. However, debate remains regarding computational fluid dynamics (cfd) and computational solid mechanics (csm) simulations due to (i) modeling assumptions and incomplete/uncertain input data derived from clinical sources and (ii) the complex interplay between hemodynamic and structural stresses, which is often overlooked. This study introduces a semi-automatic fluid-structure interaction (fsi) framework that integrates the identification of patient-specific aortic wall material properties, aiming at minimizing a primary source of uncertainty while enabling a comprehensive assessment of aortic biomechanics.

Methods

Aortic geometries, including the valve orifice plane, aortic root, and supra-aortic vessels, are reconstructed at peak systole and at end-diastole from retrospectively-gated computed tomography angiography (cta) [2]. Patient-specific transaortic jet velocity measured by Doppler echocardiography and pressure data obtained through brachial cuff measurements are used as input data. The vessel wall is modeled as a nonlinear isotropic hyperelastic Neo-Hookean solid with uniform thickness. To find patient-specific material properties, a simple iterative approach is adopted, using the ascending aorta (aao) measured systolic volume as target value for the calibration. After obtaining the diastolic tensional state through prestress simulations [4], the patient-specific systolic blood pressure is applied to the aortic wall in csm simulations. Young’s modulus is varied iteratively bisecting the initial interval 0.8-8.0 MPa [3] until the simulated aao systolic volume matches the measured target value within a 1% error. Following a prestress simulation using the previously estimated Young’s modulus, fsi simulations are conducted with a two-way arbitrary Lagrangian-Eulerian (ale) formulation. Homogeneous Dirichlet conditions are applied at the ring-shaped solid domain boundary sections in terms of null displacement. Doppler-derived flow rates are imposed in terms of flat velocity profile at the fluid inflow section. Zero-dimensional (0d) models are coupled to the 3d fluid domain outflow sections. The 0d parameters are tuned to match patient-specific cuff pressures in coupled 0d and one-dimensional (1d) simulations. All simulations are performed using the finite element-based solver SimVascular.

Results and discussion

For a representative patient, the aao peak systolic volume measured from cta was 107.60 cm³, leading to an estimated Young’s modulus of 2.15 MPa. The fsi simulation was able to capture the flow impingement region located in the anteromedial area of the bulge, where the highest values of time-averaged wall shear stress (tawss) were recorded, along with the greatest variation in wss contraction and expansion action on the luminal surface along the cardiac cycle.

By reconstructing the valve orifice, the fsi framework allows to simulate both tricuspid and bicuspid valve morphologies. This framework enables the exploration of the synergistic action of hemodynamic and structural stress, potentially revealing distinct or combined effects of these biomechanical stimuli on ataa progression.

References

1. De Nisco et al., Med Eng Phys, 2020.

2. Pasta et al., Ann Thorac Surg, 2020.

3. Trabelsi et al., Ann Biomed Eng, 2016.

4. Bäumler et al., Biomech Model Mechanobiol, 2020.

Accurately capturing the dynamics of blood flow, pressure distribution, and structural deformation within the human heart is essential for advancing our understanding of cardiovascular physiology and for improving the diagnosis and monitoring of heart disease. However, direct in-vivo measurements remain challenging: they are often invasive, limited in spatial and temporal resolution, and constrained by technical and ethical considerations. In this context, patient-specific digital twins (i.e., computational models that can reproduce individual cardiovascular anatomy and function) have emerged as a powerful tool for simulating the cardiac cycle with high fidelity.

In this work, we employ our in-house, multi-GPU simulation framework [1], designed to capture the full complexity of cardiac mechanics through a tightly integrated Fluid–Structure–Electrophysiology Interaction (FSEI) formulation. This framework incorporates a biophysically detailed electrophysiology model to simulate myocardial activation via a physiologically-informed conduction system. It also features anatomically realistic mitral and aortic valves, enabling accurate simulation of valve dynamics throughout the cardiac cycle.

A key component of our approach is the integration of patient-specific anatomical data, typically acquired at discrete time points during the cardiac cycle. These data are assimilated into the simulation to personalize the digital twin, ensuring that it reflects the subject’s unique geometry and boundary conditions. To validate this approach and evaluate its sensitivity to physiological variability, we use the full FSEI model as a reference, allowing us to assess both the realism and robustness of the personalized simulations.

To probe the complexity of intraventricular flow, we carry out a detailed Lagrangian analysis based on passive tracer dynamics. By computing statistical quantities such as structure functions, flatness, and temporal correlation functions, we characterize the multiscale features of turbulence within the left heart.
Our results show that both ventricle and aorta are marked by strong intermittency and long-lasting flow structures. We further explore how these flow characteristics are influenced by physiological parameters, including heart rate and aortic valve stiffness. Specifically, we find that a lower heart rate enhances flow intermittency by allowing coherent structures to develop and persist, while increased valve stiffness amplifies extreme fluctuations.

Overall, this study demonstrates the effectiveness of Lagrangian diagnostics in capturing the spatial and temporal complexity of cardiac flows. Our findings contribute to a deeper understanding of cardiovascular hemodynamics and highlight the potential of combining high-performance computing with clinical data to develop predictive, patient-specific digital twins of the heart.

This research was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Smart-TURB (Grant Agreement No. 882340), and by the project MUR-FARE R2045J8XAW.

[1] Viola et al., Scientific Reports, 13(1) (2023).

The complex motility patterns of the stomach, characterized by antral contractions and tonic contractions, play crucial roles in mixing, grinding, and propelling food through the gastrointestinal tract. These mechanical processes, combined with chemical digestion, break down ingested food into smaller particles, increasing surface area for enzymatic action and nutrient absorption. Computational Fluid Dynamics (CFD) has emerged as a powerful in silico tool to complement in vitro and in vivo studies, offering detailed insights into gastric flow patterns and mixing mechanisms that are difficult to measure experimentally. CFD models can simulate the fluid dynamics of gastric contents, track chemical species, and analyze the impact of varying physiological parameters on digestion efficiency.

Existing CFD models of gastric motility have primarily represented gastric contents as Newtonian fluids, with food often modelled as a chemical species rather than explicit solid particles. This simplification limits the ability to study the mechanical breakdown of solid foods, a critical aspect of gastric digestion. Furthermore, many models represent the opening and closing mechanisms of the pyloric sphincter, instead assuming a constantly open or closed pylorus. Additionally, most studies simulate only brief periods of gastric activity, typically minutes rather than the hours required for complete digestion, mainly due to the computational costs. These limitations highlight the need for more comprehensive models that capture the full complexity of digestion.

To address these limitations, we have developed a coupled particle-based model using Smoothed Particle Hydrodynamics (SPH) and Lattice Spring Model (LSM) techniques. This approach is better suited to simulate the large amplitude contractions of the stomach wall and pyloric sphincter, as well as the mechanical breakdown of solid foods. Our SPH model of gastric emptying was implemented using the open-source LAMMPS package on an idealized stomach and duodenum geometry. The model incorporates boundary deformation patterns derived from a previously developed model of gastric motility and pylorus sphincter activity, allowing for detailed simulation of gastric motility.

The SPH model successfully captured key gastric flow patterns, including retropulsive jets and recirculation regions, which are essential for effective mixing and emptying. The simulated half-emptying time for water (13 minutes) aligns closely with in-vivo human studies. The ability of the CFD model to represent pyloric sphincter dynamics provides a more accurate representation of gastric emptying regulation. The solid-phase of digesta is represented using LSM to explicitly model food particle breakdown.

Future work includes developing subject-specific models using MRI data from an ongoing study investigating the gastric digestion of halloumi cheese. This study simultaneously measures motility patterns and digestion kinetics, providing valuable validation data for our SPH-LSM coupled model. Our approach offers a significant advancement in computational modelling of gastric function, bridging the gap between micro-scale processes and whole-organ mechanics, and providing new insights into smooth muscle organ dynamics.