Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images.

One-dimensional (1D) cardiovascular models offer a non-invasive method to answer medical questions, including predictions of wave-reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient-specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty in in vivo imaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image-derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three-dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high-fidelity patient-specific haemodynamics models. KEY POINTS: This study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error. Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks. Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty. Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii.

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling.

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.

An investigation into the design of a device to treat haemorrhagic stroke.

In this study, we present the design considerations of a device to assist in the potential treatment of hemorrhagic stroke with the aim of stopping blood from flowing out into brain tissue. We present and model three designs for the clinical scenarios when saccular aneurysms rupture in the middle cerebral artery in the brain. We evaluate and model these three designs using computer aided design software, SolidWorks, which allows the devices to be tested using finite element analysis and also enables us to justify that the materials chosen were suitable for potential use. Computational fluid dynamics modelling were used to demonstrate and analyse the flow of blood through the artery under conditions of normal and ruptured states. We conclude that our device could potentially be useful in the treatment of hemorrhagic stroke, and the modelling process is useful in assisting in determining the performance of our devices.

Haemodynamic mechanism of formation and distribution of coronary atherosclerosis: A lesion-specific model.

Coronary arterial disease, as the most devastated cardiovascular disease, is caused by the atherosclerosis in the coronary arteries, which blocks the blood flow to the heart, resulting in the deficient supply of oxygen and nutrition to the heart, and eventually leading to heart failure. To date, haemodynamic mechanisms for atherosclerosis development are not fully understood although it is believed that the haemodynamic disturbance at the region of the arterial bifurcation, particular, bifurcation angle, plays an important role in the atherosclerosis development. In this study, two types of computational fluid dynamics models, lesion-specific and idealized models, combined with the computer tomography imaging techniques, are used to explore the mechanism of formation and distribution of the atherosclerosis around the bifurcation of left coronary artery and its association with the bifurcation angle. The lesion-specific model is used to characterize the effect of personalized features on the haemodynamic performance, while the idealized model is focusing on the effect of single factor, bifurcation angle, on the haemodynamic performance. The simulated results from both types of the models, combined with the clinical observation, revealed that the three key areas around the bifurcations are prone to formation of the atherosclerosis. Unlike the idealized models, lesion-specific modelling results did not show the significant correlation between the wall shear stress and bifurcation angle, although the mean value of the wall shear stress in smaller bifurcation angles (less than 90°) is higher than that with larger bifurcation angles (greater than 90°). In conclusion, lesion-specific computational fluid dynamics modelling is an efficient and convenient way to predict the haemodynamic performance around the bifurcation region, allowing the comprehensive information for the clinicians to predict the atherosclerosis development. The idealized models, which only focus on single parameter, may not provide the sufficient and reliable information for the clinical application. A novel multi-parameters modelling technique, therefore, is suggested to be developed in future, allowing the effects of many parameters on the haemodynamic performance to be evaluated.

Sustained high-pressure in the spinal subarachnoid space while arterial expansion is low may be linked to syrinx development.

Syringomyelia (a spinal cord cyst) usually develops as a result of conditions that cause cerebrospinal fluid (CSF) obstruction. The mechanism of syrinx formation and enlargement remains unclear, though previous studies suggest that the fluid enters via the perivascular spaces (PVS) of the penetrating arteries of the spinal cord, and that alterations in the CSF pulse timing and pressure could contribute to enhanced PVS inflow. This study uses an idealised computational model of the PVS to investigate the factors that influence peri-arterial fluid flow. First, we used three sample patient-specific models to explore whether changes in subarachnoid space (SAS) pressures in individuals with and without syringomyelia could influence PVS inflow. Second we conducted a parametric study to determine how features of the CSF pulse altered perivascular fluid, including alterations to timing and magnitude of the peak SAS pressure, the timing of reversal from high to low pressure (diastolic phase), and the area under the pressure-time curve. The model for the patient with syringomyelia had higher net CSF inflow to the PVS than the two subjects without syringomyelia. In the parametric study, only increasing the area under the high pressure region of the SAS pulse substantially increased PVS inflow, when coupled with a temporal shift in arterial and SAS pulses. This suggests that a period of sustained high SAS pressure while arterial diameter is low may increase net CSF pumping into the PVS.

Fluid dynamics simulation of right ventricular outflow tract oversizing.

OBJECTIVES: Repair of the right ventricular outflow tract (RVOT) in paediatric cardiac surgery remains challenging due to the high reoperation rate. Intimal hyperplasia and consequent arteriosclerosis is one of the most important limitation factors for graft durability. Since local shear stress and pressure are predictive elements for intimal hyperplasia and wall degeneration, we sought to determine in an oversized 12-mm RVOT model, with computed fluid dynamics simulation, the local haemodynamical factors that may explain intimal hyperplasia. This was done with the aim of identifying the optimal degree of oversizing for a 12-mm native RVOT. METHODS: Twenty domestic pigs, with a weight of 24.6 ± 0.89 kg and a native RVOT diameter of 12 ± 1.7 mm, had valve conduits of 12, 16, 18 and 20 mm implanted. Pressure and flow were measured at 75, 100 and 125% of normal flow at RVOT at the pulmonary artery, pulmonary artery bifurcation and at the left and right pulmonary arteries. Three-dimensional computed fluid dynamics (CFD) simulation in all four geometries in all flow modalities was performed. Local shear stress and pressure conditions were investigated. RESULTS: Corresponding to 75, 100 and 125% of steady-state flow, three inlet velocity profiles were obtained, 0.2, 0.29 and 0.36 m/s, respectively. At inflow velocity profiles, low shear stress areas, ranged from 0 to 2 Pa, combined with high-pressure areas ranging from 11.5 to 12.1 mmHg that were found at distal anastomosis, at bifurcation and at the ostia of the left and right pulmonary arteries in all geometries. CONCLUSIONS: In all three oversized geometries, the local reparation of shear stress and pressure in the 16-mm model showed a similar local profile as in the native 12 mm RVOT. According to these findings, we suggest oversizing the natural 12-mm RVOT by not more than 4 mm. The elements responsible for wall degeneration and intimal hyperplasia remain very similar to the conditions present in native RVOT.

Computational fluid dynamics of the right ventricular outflow tract and of the pulmonary artery: a bench model of flow dynamics.

OBJECTIVES: The reconstruction of the right ventricular outflow tract (RVOT) with valved conduits remains a challenge. The reoperation rate at 5 years can be as high as 25% and depends on age, type of conduit, conduit diameter and principal heart malformation. The aim of this study is to provide a bench model with computer fluid dynamics to analyse the haemodynamics of the RVOT, pulmonary artery, its bifurcation, and left and right pulmonary arteries that in the future may serve as a tool for analysis and prediction of outcome following RVOT reconstruction. METHODS: Pressure, flow and diameter at the RVOT, pulmonary artery, bifurcation of the pulmonary artery, and left and right pulmonary arteries were measured in five normal pigs with a mean weight of 24.6 ± 0.89 kg. Data obtained were used for a 3D computer fluid-dynamics simulation of flow conditions, focusing on the pressure, flow and shear stress profile of the pulmonary trunk to the level of the left and right pulmonary arteries. RESULTS: Three inlet steady flow profiles were obtained at 0.2, 0.29 and 0.36 m/s that correspond to the flow rates of 1.5, 2.0 and 2.5 l/min flow at the RVOT. The flow velocity profile was constant at the RVOT down to the bifurcation and decreased at the left and right pulmonary arteries. In all three inlet velocity profiles, low sheer stress and low-velocity areas were detected along the left wall of the pulmonary artery, at the pulmonary artery bifurcation and at the ostia of both pulmonary arteries. CONCLUSIONS: This computed fluid real-time model provides us with a realistic picture of fluid dynamics in the pulmonary tract area. Deep shear stress areas correspond to a turbulent flow profile that is a predictive factor for the development of vessel wall arteriosclerosis. We believe that this bench model may be a useful tool for further evaluation of RVOT pathology following surgical reconstructions.