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 <2.0cm2. 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.

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

Fractional flow reserve (FFR)-guided percutaneous intervention is superior to standard assessment but remains underused. The authors have developed a novel "pseudotransient" analysis protocol for computing virtual fractional flow reserve (vFFR) based upon angiographic images and steady-state computational fluid dynamics. This protocol generates vFFR results in 189 s (cf >24 h for transient analysis) using a desktop PC, with <1% error relative to that of full-transient computational fluid dynamics analysis. Sensitivity analysis demonstrated that physiological lesion significance was influenced less by coronary or lesion anatomy (33%) and more by microvascular physiology (59%). If coronary microvascular resistance can be estimated, vFFR can be accurately computed in less time than it takes to make invasive measurements.

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.

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.

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.