Angela Lungu

Computational fluid dynamics modelling in cardiovascular medicine

This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards ‘digital patient’ or ‘virtual physiological human’ representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.

MRI model-based non-invasive differential diagnosis in pulmonary hypertension

Pulmonary hypertension(PH) is a disorder characterised by increased mean pulmonary arterial pressure. Currently, the diagnosis of PH relies upon measurements taken during invasive right heart catheterisation (RHC). This paper describes a process to derive diagnostic parameters using only non-invasive methods based upon MRI imaging alone. Simultaneous measurements of main pulmonary artery (MPA) anatomy and flow are interpreted by 0D and 1D mathematical models, in order to infer the physiological status of the pulmonary circulation. Results are reported for 35 subjects, 27 of whom were patients clinically investigated for PH and eight of whom were healthy volunteers. The patients were divided into 3 sub-groups according to the severity of the disease state, one of which represented a negative diagnosis (NoPH), depending on the results of the clinical investigation, which included RHC and complementary MR imaging. Diagnostic indices are derived from two independent mathematical models, one based on the 1D wave equation and one based on an RCR Windkessel model. Using the first model it is shown that there is an increase in the ratio of the power in the reflected wave to that in the incident wave (Wpb/Wptotal) according to the classification of the disease state. Similarly, the second model shows an increase in the distal resistance with the disease status. The results of this pilot study demonstrate that there are statistically significant differences in the parameters derived from the proposed models depending on disease status, and thus suggest the potential for development of a non-invasive, image-based diagnostic test for pulmonary hypertension.

Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis: Results From the VIRTU-Fast Study

Visual Abstract

Heart rate reduction with ivabradine promotes shear stress-dependent anti-inflammatory mechanisms in arteries

Blood flow generates wall shear stress (WSS) which alters endothelial cell (EC) function. Low WSS promotes vascular inflammation and atherosclerosis whereas high uniform WSS is protective. Ivabradine decreases heart rate leading to altered haemodynamics. Besides its cardio-protective effects, ivabradine protects arteries from inflammation and atherosclerosis via unknown mechanisms. We hypothesised that ivabradine protects arteries by increasing WSS to reduce vascular inflammation. Hypercholesterolaemic mice were treated with ivabradine for seven weeks in drinking water or remained untreated as a control. En face immunostaining demonstrated that treatment with ivabradine reduced the expression of pro-inflammatory VCAM-1 (p<0.01) and enhanced the expression of anti-inflammatory eNOS (p<0.01) at the inner curvature of the aorta. We concluded that ivabradine alters EC physiology indirectly via modulation of flow because treatment with ivabradine had no effect in ligated carotid arteries in vivo, and did not influence the basal or TNFα-induced expression of inflammatory (VCAM-1, MCP-1) or protective (eNOS, HMOX1, KLF2, KLF4) genes in cultured EC. We therefore considered whether ivabradine can alter WSS which is a regulator of EC inflammatory activation. Computational fluid dynamics demonstrated that ivabradine treatment reduced heart rate by 20 % and enhanced WSS in the aorta. In conclusion, ivabradine treatment altered haemodynamics in the murine aorta by increasing the magnitude of shear stress. This was accompanied by induction of eNOS and suppression of VCAM-1, whereas ivabradine did not alter EC that could not respond to flow. Thus ivabradine protects arteries by altering local mechanical conditions to trigger an anti-inflammatory response.

Diagnosis of pulmonary hypertension from magnetic resonance imaging–based computational models and decision tree analysis

Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image-based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy-two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty-seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2 mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image-based markers. Classifier results, validated using leave-one-out cross validation, demonstrated that combining computation-derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI-based model parameters may reduce the need for RHC in patients with suspected PH.

Automatic, Simultaneous, Non-invasive Measurements of Flow and Area in the Human Pulmonary Arteries from MRI Images

Pulmonary hypertension (PH), a disease with a high mortality rate, is currently diagnosed by invasive right heart catheterization (RHC). Quantification of wave reflections can offer information about the status of the pulmonary circulation in health and disease, and can be achieved by simultaneous measurement of flow and pressure at the same anatomical site. In practice such measurements are obtained using different acquisition techniques which needs to be synchronized in order to satisfy the simultaneity criterion. We hypothesis that combining the advantages of mathematical modelling with non-invasive Magnetic Resonance Imaging (MRI) measurements of flow and anatomy could offer totally non-invasive modalities for characterizing PH. Our preliminary work was focused on the development of automatic tools to determine accurately flow Q(t) and area A(t) measurements directly from MRI DICOM images, ultimately to support a novel characterisation protocol although the latter is beyond the scope of this paper.

Comparison of CFD-Based and Bernoulli-Based Pressure Drop Estimates across the Aortic Valve Enabled by Shape-Constrained Deformable Segmentation of Cardiac CT Images

The aortic valve area (AVA) and the pressure drop (PD) across the aortic valve are important quantities for characterizing an aortic valve stenosis. Using the Bernoulli equation and mass conservation, a relation between both quantities can be derived. We developed a simulation pipeline to assess the accuracy of this relation for realistic patient anatomies and blood flow rates. The key element of the pipeline is a shape-constrained deformable model (SCDM) for the segmentation of the aortic valve, the ascending aorta and the left ventricle over the cardiac cycle in cardiac CT images. Efficient segmentation enabled application of the simulation pipeline to cardiac CT image sequences of 22 patients. Planimetric AVA and Bernoulli-based PD estimates were computed from the same segmentation results. The resulting PD estimates show a high correlation (R = 0.97), but Bernoulli-based PD results are on average 25% smaller than the CFD-based results. The results contribute to a better understanding and interpretation of clinically used quantities such as the AVA and the PD.

CFD‐ and Bernoulli‐based pressure drop estimates: A comparison using patient anatomies from heart and aortic valve segmentation of CT images

Purpose An aortic valve stenosis is an abnormal narrowing of the aortic valve (AV). It impedes blood flow and is often quantified by the geometric orifice area of the AV (AVA) and the pressure drop (PD). Using the Bernoulli equation, a relation between the PD and the effective orifice area (EOA) represented by the area of the vena contracta (VC) downstream of the AV can be derived. We investigate the relation between the AVA and the EOA using patient anatomies derived from cardiac computed tomography (CT) angiography images and computational fluid dynamic (CFD) simulations. Methods We developed a shape‐constrained deformable model for segmenting the AV, the ascending aorta (AA), and the left ventricle (LV) in cardiac CT images. In particular, we designed a structured AV mesh model, trained the model on CT scans, and integrated it with an available model for heart segmentation. The planimetric AVA was determined from the cross‐sectional slice with minimum AV opening area. In addition, the AVA was determined as the nonobstructed area along the AV axis by projecting the AV leaflet rims on a plane perpendicular to the AV axis. The flow rate was derived from the LV volume change. Steady‐state CFD simulations were performed on the patient anatomies resulting from segmentation. Results Heart and valve segmentation was used to retrospectively analyze 22 cardiac CT angiography image sequences of patients with noncalcified and (partially) severely calcified tricuspid AVs. Resulting AVAs were in the range of 1–4.5 cm2 and ejection fractions (EFs) between 20 and 75%. AVA values computed by projection were smaller than those computed by planimetry, and both were strongly correlated (R2 = 0.995). EOA values computed via the Bernoulli equation from CFD‐based PD results were strongly correlated with both AVA values (R2 = 0.97). EOA values were ∼10% smaller than planimetric AVA values. For EOA values < 2.0 cm2, the EOA was up to ∼15% larger than the projected AVA. Conclusions The presented segmentation algorithm allowed to construct detailed AV models for 22 patient cases. Because of the crown‐like 3D structure of the AV, the planimetric AVA is larger than the projected AVA formed by the free edges of the AV leaflets. The AVA formed by the free edges of the AV leaflets was smaller than the EOA for EOA values Symbol. This contradiction with respect to previous studies that reported the EOA to be always smaller or equal to the geometric AVA is explained by the more detailed AV models used within this study. Symbol. No caption available.