Computational model of the fetal heart with Coarctation of the Aorta

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Directors: Patricia Garcia Cañadilla & Bart Bijnens. Tutora: Fátima Crispi.It is thought that altered intrauterine hemodynamics may lead to congenital heart defects, such as aortic arch abnormalities. Coarctation of the aorta (CoA) is one of the most difficult cardiac defects to diagnose before birth, because of the patency of the ductus arteriosus (DA). It consists of a narrowing in the aortic isthmus (AoI) causing a decrease of blood flow. Prenatal diagnosis is important to reduce mortality and morbidity. Nonetheless, prenatal diagnosis has a high rate of false-positive and false-negatives and local hemodynamics in the CoA is not fully understood. The aim of this project was to improve our understanding of the underlying cause of CoA using computational fluid dynamics (CFD) tools. We have implemented a computational model with an idealized geometry of the fetal aorta to investigate the relationship between flow unbalance and wall shear stress (WSS) at the isthmus-ductus. An imbalanced flow was imposed in the ascending aorta (AscAo) and ductus to study if a progressive aortic flow reduction suggests the “flowdependency” of the fetal aortic arch development. As a result, when aortic flow diminished from 50% to 10% progressively, velocity and WSS decreased in the aortic arch and increased in the distal arch. A redistribution of flow could be observed in the model and a “zero flow zone” could be noticed between the brachiocephalic artery and left carotid when the flow decreased to from 50% to 10%. Additionally, another “zero flow zone” could be observed in the AoI when the aortic flow decreased from 50% to 30%

Differential impact of local stiffening and narrowing on hemodynamics in repaired aortic coarctation: an FSI study

Even after successful treatment of aortic coarctation, a high risk of cardiovascular morbidity and mortality remains. Uncertainty exists on the factors contributing to this increased risk among which are the presence of (1) a residual narrowing leading to an additional resistance and (2) a less distensible zone disturbing the buffer function of the aorta. As the many interfering factors and adaptive physiological mechanisms present in vivo prohibit the study of the isolated impact of these individual factors, a numerical fluid-structure interaction model is developed to predict central hemodynamics in coarctation treatment. The overall impact of a stiffening on the hemodynamics is limited, with a small increase in systolic pressure (up to 8 mmHg) proximal to the stiffening which is amplified with increasing stiffening and length. A residual narrowing, on the other hand, affects the hemodynamics significantly. For a short segment (10 mm), the combination of a stiffening and narrowing (coarctation index 0.5) causes an increase in systolic pressure of 58 mmHg, with 31 mmHg due to narrowing and an additional 27 mmHg due to stiffening. For a longer segment (25 mm), an increase in systolic pressure of 50 mmHg is found, of which only 9 mmHg is due to stiffening

Incorporating the Aortic Valve into Computational Fluid Dynamics Models using Phase-Contrast MRI and Valve Tracking

The American Heart Association states about 2% of the general population have a bicuspid aortic valve (BAV). BAVs exist in 80% of patients with aortic coarctation (CoA) and likely influences flow patterns that contribute to long-term morbidity post-surgically. BAV patients tend to have larger ascending aortic diameters, increased risk of aneurysm formation, and require surgical intervention earlier than patients with a normal aortic valve. Magnetic resonance imaging (MRI) has been used clinically to assess aortic arch morphology and blood flow in these patients. These MRI data have been used in computational fluid dynamics (CFD) studies to investigate potential adverse hemodynamics in these patients, yet few studies have attempted to characterize the impact of the aortic valve on ascending aortic hemodynamics. To address this issue, this research sought to identify the impact of aortic valve morphology on hemodynamics in the ascending aorta and determine the location where the influence is negligible. Novel tools were developed to implement aortic valve morphology into CFD models and compensate for heart motion in MRI flow measurements acquired through the aortic valve. Hemodynamic metrics such as blood flow velocity, time-averaged wall shear stress (TAWSS), and turbulent kinetic energy (TKE) induced by the valve were compared to values obtained using the current plug inflow approach. The influence of heart motion on these metrics was also investigated, resulting in the underestimation of TAWSS and TKE when heart motion was neglected. CFD simulations of CoA patients exhibiting bicuspid and tricuspid aortic valves were performed in models including the aortic sinuses and patient-specific valves. Results indicated the aortic valve impacted hemodynamics primarily in the ascending aorta, with the BAV having the greatest influence along the outer right wall of the vessel. A marked increase in TKE is present in aortic valve simulations, particularly in BAV patients. These findings suggest that future CFD studies investigating altered hemodynamics in the ascending aorta should accurately replicate aortic valve morphology. Further, aortic valve disease impacts hemodynamics in the ascending aorta that may be a predictor of the development or progression of ascending aortic dilation and possible aneurysm formation in this region

Computational fluid dynamics (CFD) study of patients suffering coarctation of the aorta (CoA) for pre and post-operative

Coarctation of the aorta (CoA) is a widespread anomaly that occurs a lot in infants. CoA affects the human health. It causes hypertension, decrease in the amount of blood flow and heart failure. CoA is related to abnormal hemodynamics and certain blood flow patterns are noticed. Different surgical techniques are implemented in order to increase the amount of blood flow such resection end-to-end anastomosis, resection end-to-side anastomosis…etc. This research aims at identifying the effect of CoA on the flow pattern and quantification of the improvement after surgery through utilizing computational fluid dynamics (CFD) to solve flow fields in the aorta. CFD is applied on a real geometry of the aorta are obtained by computerized tomography (CT) scan for five pre and post-operative patients. The boundary conditions are derived from the phase contrast magnetic resonance imaging (PC-MRI). Then, grid independence and time sensitivity analysis are performed. Flow patterns are judged visually by comparing the contours of the streamlines, vortex core, pressure and the time averaged wall shear stress (TAWSS). In order to quantify the flow fields and the improvement as well, different flow variables are used such as Womersley number, Strouhal number and specific turbulent kinetic energy. The wall shear stress at peak systole and the amount of the blood flow in the direction of the vessel’s centerline are used as a measure of improvement. The results of the CFD showed that blood flow patterns are highly dependent on the geometry of the vessel. For a CoA, jet formulation then break up, backflow and chaotic behavior exists after the area of the disease. In addition, a high concentrated wall shear stress is around the area of the CoA. For post-op, the change of the area because of the surgery produced separation. For both pre and post-op, the angle between the velocity vector at the inlet and the centerline of the vessel resulted in a jet impingement and very high wall shear stress. On the other hand, the specific turbulence kinetic energy and the wall shear stress is higher after the surgery. Strouhal number in the descending aorta has decreased after the operation except for one patient. The amount of blood flow increased after the surgery. Blood flow in the downstream became attached to the vessel. Finally, the flow fields are sensitive to the turbulence model; however, they did not show significant dependence on the viscosity model. The turbulence effects cannot be neglected due to their significant contribution to the velocity field

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy

Tuning of boundary conditions parameters for hemodynamics simulation using patient data

This thesis describes an engineering workflow, which allows specification of boundary conditions and 3D simulation based on clinically available patient-specific data. A review of numerical models used to describe the cardiovascular system is provided, with a particular focus on the clinical target disease chosen for the toolkit, aortic coarctation. Aorta coarctation is the fifth most common congenital heart disease, characterized by a localized stenosis of the descending thoracic aorta. Current diagnosis uses invasive pressure measurement with rare but potential complications. The principal objective of this work was to develop a tool that can be translated into the clinic, requiring minimum operator input and time, capable of returning meaningful results from data typically acquired in clinical practice. Linear and nonlinear 1D modelling approaches are described, tested against full 3D solutions derived for idealized geometries of increasing complexityand for a patient-specific aortic coarctation. The 1D linear implementation is able to represent the fluid dynamic in simple idealized benchmarks with a limited effort in terms of computational time, but in a more complex case, such as a mild aortic coarctation, it is unable to predict well 3D fluid dynamic features. On the other side, the 1D nonlinear implementation showed a good agreement when compared to 3D pressure and flow waveforms, making it suitable to estimate outflow boundary conditions for subject-specific models. A cohort of 11 coarctation patients was initially used for a preliminary analysis using 0D models of increasing complexity to examine parameters derived when tuning models of the peripheral circulation. The first circuit represents the aortic coarctation as a nonlinear resistance, using the Bernoulli pressure drop equation, without considering the effect of downstream circulation. The second circuit include a peripheral resistance and compliance, and separate ascending and descending aortic pressure responses. In the third circuit a supra-aortic Windkessel model was added in order to include the supra-aortic circulation. The analysis detailed represents a first attempt to assess the interaction between local aortic haemodynamics and subject-specific parameterization of windkessel representations of the peripheral and supra-aortic circulation using clinically measured data. From the analysis of these 0D models, it is clear that the significance of the coarctation becomes less from the simple two resistance model to the inclusion of both the peripheral and supra-aortic circulation. These results provide a context within which to interpret outcomes of the tuning process reported for a more complex model of aortic haemodynamics using 1D and 3D model approaches. Earlier developments are combined to enable a multi-scale modelling approach to simulate fluid-dynamics. This includes non-linear 1D models to derive patient-specific parameters for the peripheral and supra-aortic circulation followed by transient analysis of a coupled 3D/0D system to estimate the coarctation pressure augmentation. These predictions are compared with invasively measured catheter data and the influence of uncertainty in measured data on the tuning process is discussed. This study has demonstrated the feasibility of constructing a workflow using non-invasive routinely collected clinical data to predict the pressure gradient in coarctation patients using patient specific CFD simulation, with relatively low levels of user interaction required. The results showed that the model is not suitable for the clinical use at this stage, thus further work is required to enhance the tuning process to improve agreement with measured catheter data. Finally, a preliminary approach for the assessment of change in haemodynamics following coarctation repair, where the coarctation region is enlarged through a virtual intervention process. The CFD approach reported can be expanded to explore the sensitivity of the peak ascending aortic pressure and descending aortic flow to the aortic diameter achieved following intervention, such an analysis would provide guidance for surgical intervention to target the optimal diameter to restore peripheral perfusion and reduce cerebral hypertension

Numerical modelling of the fluid-structure interaction in complex vascular geometries

A complex network of vessels is responsible for the transportation of blood throughout the body and back to the heart. Fluid mechanics and solid mechanics play a fundamental role in this transport phenomenon and are particularly suited for computer simulations. The latter may contribute to a better comprehension of the physiological processes and mechanisms leading to cardiovascular diseases, which are currently the leading cause of death in the western world. In case these computational models include patient-specific geometries and/or the interaction between the blood flow and the arterial wall, they become challenging to develop and to solve, increasing both the operator time and the computational time. This is especially true when the domain of interest involves vascular pathologies such as a local narrowing (stenosis) or a local dilatation (aneurysm) of the arterial wall. To overcome these issues of high operator times and high computational times when addressing the bio(fluid)mechanics of complex geometries, this PhD thesis focuses on the development of computational strategies which improve the generation and the accuracy of image-based, fluid-structure interaction (FSI) models. First, a robust procedure is introduced for the generation of hexahedral grids, which allows for local grid refinements and automation. Secondly, a straightforward algorithm is developed to obtain the prestress which is implicitly present in the arterial wall of a – by the blood pressure – loaded geometry at the moment of medical image acquisition. Both techniques are validated, applied to relevant cases, and finally integrated into a fluid-structure interaction model of an abdominal mouse aorta, based on in vivo measurements

MRI-based computational hemodynamics in patients with aortic coarctation using the lattice Boltzmann methods : Clinical validation study

PURPOSE: To introduce a scheme based on a recent technique in computational hemodynamics, known as the lattice Boltzmann methods (LBM), to noninvasively measure pressure gradients in patients with a coarctation of the aorta (CoA). To provide evidence on the accuracy of the proposed scheme, the computed pressure drop values are compared against those obtained using the reference standard method of catheterization. MATERIALS AND METHODS: Pre‐ and posttreatment LBM‐based pressure gradients for 12 patients with CoA were simulated for the time point of peak systole using the open source library OpenLB. Four‐dimensional (4D) flow‐sensitive phase‐contrast MRI at 1.5 Tesla was used to acquire flow and to setup the simulation. The vascular geometry was reconstructed using 3D whole‐heart MRI. Patients underwent pre‐ and postinterventional pressure catheterization as a reference standard. RESULTS: There is a significant linear correlation between the pretreatment catheter pressure drops and those computed based on the LBM simulation, [Formula: see text] , [Formula: see text]. The bias was ‐0.58 ± 4.1 mmHg and was not significant ( [Formula: see text] with a 95% confidence interval (CI) of ‐3.22 to 2.06. For the posttreatment results, the bias was larger and at ‐2.54 ± 3.53 mmHg with a 95% CI of ‐0.17 to ‐4.91 mmHg. CONCLUSION: The results indicate a reasonable agreement between the simulation results and the catheter measurements. LBM‐based computational hemodynamics can be considered as an alternative to more traditional computational fluid dynamics schemes for noninvasive pressure calculations and can assist in diagnosis and therapy planning. Level of Evidence: 3 J. Magn. Reson. Imaging 2017;45:139–146