D. Rodney Hose

Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study.

OBJECTIVES The aim of this study was to develop a computer model that accurately predicts myocardial fractional flow reserve (FFR) from angiographic images alone, in patients with coronary artery disease. BACKGROUND Percutaneous coronary intervention (PCI) guided by FFR is superior to standard assessment alone. FFR-guided PCI results in improved clinical outcomes, a reduction in the number of stents implanted, and reduced cost. Currently FFR is used in few patients. A less invasive FFR would be a valuable tool. METHODS Nineteen patients with stable coronary artery disease awaiting elective PCI were studied. They underwent rotational coronary angiography. The FFR was measured, physiologically significant lesions were stented, and angiography and FFR were repeated. Three-dimensional arterial anatomy pre- and post-stenting was reconstructed offline. Generic boundary conditions for computational fluid dynamics analysis were applied. The virtual fractional flow reserve (vFFR) and measured fractional flow reserve (mFFR) values were compared. RESULTS Thirty-five matched anatomical and physiological datasets were obtained: 10 right coronary arteries (RCA) (5 pre- and post-stenting), and 12 left coronary arteries (LCA) (8 pre- and post-stenting). The computational fluid dynamics model predicted which lesions were physiologically significant (FFR <0.80) and which were not (FFR >0.80) with accuracy, sensitivity, specificity, positive and negative predictive values of 97%, 86%, 100%, 100%, and 97% respectively. On average, the vFFR values deviated from mFFR by ±0.06 (mean delta = 0.02, SD = 0.08). The vFFR and mFFR were closely correlated (r = 0.84). CONCLUSIONS We have developed a model of intracoronary physiology based upon a rotational coronary angiogram. Significant lesions were identified with 97% accuracy. The FFR was reliably predicted without the need for invasive measurements or inducing hyperemia.

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.

A Complex Automata approach for in-stent restenosis: two-dimensional multiscale modelling and simulations

In-stent restenosis, the maladaptive response of a blood vessel to injury caused by the deployment of a stent, is a multiscale system involving a large number of biological and physical processes. We describe a Complex Automata model for in-stent restenosis, coupling bulk flow, drug diffusion, and smooth muscle cell models, all operating on different time scales. Details of the single scale models and of the coupling interfaces are described, together with first simulation results, obtained with a dedicated software environment for Complex Automata simulations. Preliminary results show that the model can reproduce growth trends observed in experimental studies and facilitate testing of hypotheses concerning the interaction of key factors.

Validation of Non-rigid Registration Using Finite Element Methods

We present a novel validation method for non-rigid registration using a simulation of deformations based on biomechanical modelling of tissue properties. This method is tested on a previously developed non-rigid registration method for dynamic contrast enhanced Magnetic Resonance (MR) mammography image pairs [1]. We have constructed finite element breast models and applied a range of displacements to them, with an emphasis on generating physically plausible deformations which may occur during normal patient scanning procedures. From the finite element method (FEM) solutions, we have generated a set of deformed contrast enhanced images against which we have registered the original dynamic image pairs. The registration results have been successfully validated at all breast tissue locations by comparing the recovered displacements with the biomechanical displacements. The validation method presented in this paper is an important tool to provide biomechanical gold standard deformations for registration error quantification, which may also form the basis to improve and compare different non-rigid registration techniques for a diversity of medical applications.

Effects of smoking and hypertension on wall shear stress and oscillatory shear index at the site of intracranial aneurysm formation.

OBJECTIVE The mechanisms by which smoking and hypertension lead to increased incidence of intracranial aneurysm (IA) formation remain poorly understood. The current study investigates the effects of these risk factors on wall shear stress (WSS) and oscillatory shear index (OSI) at the site of IA initiation. METHODS Two (n=2) IAs from two patients with history of smoking and hypertension were artificially removed with the help of software @neuFuse (Supercomputing Solutions, Bologna, Italy) and the vessel geometry reconstructed to mimic the condition prior to IA formation. Two computational fluid dynamics (CFD) analyses were performed on each data-set by using in turn the normal physiological values of blood viscosity (BV), and high BV values specific to smoking and hypertension, obtained from literature. RESULTS At normal BV, high WSS (>15 Pa) was observed at the site of IA initiation in both patients. When BV values specific to smoking and hypertension were used, both the areas affected by high WSS (>15 Pa) and the maximum WSS were increased whilst the magnitude and distribution of OSI showed no significant change. CONCLUSIONS Long-term exposure to high WSS may result in an increased risk of IA development. An incremental increase in areas of high WSS observed secondary to smoking and hypertension may indicate a further increase in the risk of IA initiation. Interestingly, the relationship between BV and the area of increased WSS was not linear, reflecting the need for patient-specific CFD analysis.

Accuracy vs. computational time: Translating aortic simulations to the clinic

State of the art simulations of aortic haemodynamics feature full fluid-structure interaction (FSI) and coupled 0D boundary conditions. Such analyses require not only significant computational resource but also weeks to months of run time, which compromises the effectiveness of their translation to a clinical workflow. This article employs three computational fluid methodologies, of varying levels of complexity with coupled 0D boundary conditions, to simulate the haemodynamics within a patient-specific aorta. The most comprehensive model is a full FSI simulation. The simplest is a rigid walled incompressible fluid simulation while an alternative middle-ground approach employs a compressible fluid, tuned to elicit a response analogous to the compliance of the aortic wall. The results demonstrate that, in the context of certain clinical questions, the simpler analysis methods may capture the important characteristics of the flow field.

virtual (Computed) fractional flow reserve current challenges and limitations

Fractional flow reserve (FFR) is the “gold standard” for assessing the physiological significance of coronary artery disease during invasive coronary angiography. FFR-guided percutaneous coronary intervention improves patient outcomes and reduces stent insertion and cost; yet, due to several practical and operator related factors, it is used in <10% of percutaneous coronary intervention procedures. Virtual fractional flow reserve (vFFR) is computed using coronary imaging and computational fluid dynamics modeling. vFFR has emerged as an attractive alternative to invasive FFR by delivering physiological assessment without the factors that limit the invasive technique. vFFR may offer further diagnostic and planning benefits, including virtual pullback and virtual stenting facilities. However, there are key challenges that need to be overcome before vFFR can be translated into routine clinical practice. These span a spectrum of scientific, logistic, commercial, and political areas. The method used to generate 3-dimensional geometric arterial models (segmentation) and selection of appropriate, patient-specific boundary conditions represent the primary scientific limitations. Many conflicting priorities and design features must be carefully considered for vFFR models to be sufficiently accurate, fast, and intuitive for physicians to use. Consistency is needed in how accuracy is defined and reported. Furthermore, appropriate regulatory and industry standards need to be in place, and cohesive approaches to intellectual property management, reimbursement, and clinician training are required. Assuming successful development continues in these key areas, vFFR is likely to become a desirable tool in the functional assessment of coronary artery disease.

Comparison of biomechanical breast models: a case study

We present initial results from evaluating the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast. We investigate the influence of different tissue elasticity values, Poissons ratios, boundary conditions, finite element solvers and mesh resolutions on one data set. MR images were acquired before and after compressing a volunteers breast gently. These images were aligned using a 3D non-rigid registration algorithm. The boundary conditions were derived from the result of the non-rigid registration or by assuming no patient motion at the deep or medial side. Three linear and two non-linear elastic material models were tested. The accuracy of the BBMs was assessed by the Euclidean distance of twelve corresponding anatomical landmarks. Overall, none of the tested material models was obviously superior to another regarding the set of investigated values. A major average error increase was noted for partially inaccurate boundary conditions at high Poissons ratios due to introduced volume change. Maximal errors remained, however, high for low Poissons ratio due to the landmarks closeness to the inaccurate boundary conditions. The choice of finite element solver or mesh resolution had almost no effect on the performance outcome.

Computational modelling and evaluation of cardiovascular response under pulsatile impeller pump support

This study presents a numerical simulation of cardiovascular response in the heart failure condition under the support of a Berlin Heart INCOR impeller pump-type ventricular assist device (VAD). The model is implemented using the CellML modelling language. To investigate the potential of using the Berlin Heart INCOR impeller pump to produce physiologically meaningful arterial pulse pressure within the various physiological constraints, a series of VAD-assisted cardiovascular cases are studied, in which the pulsation ratio and the phase shift of the VAD motion profile are systematically changed to observe the cardiovascular responses in each of the studied cases. An optimization process is proposed, including the introduction of a cost function to balance the importance of the characteristic cardiovascular variables. Based on this cost function it is found that a pulsation ratio of 0.35 combined with a phase shift of 200° produces the optimal cardiovascular response, giving rise to a maximal arterial pulse pressure of 12.6 mm Hg without inducing regurgitant pump flow while keeping other characteristic cardiovascular variables within appropriate physiological ranges.

Towards a Complex Automata Multiscale Model of In-Stent Restenosis

In-stent restenosis, the maladaptive response of a blood vessel to injury caused by the deployment of a stent, is a multiscale problem involving a large number of processes. We describe a Complex Automata Model for in-stent restenosis, coupling a bulk flow, drug diffusion, and smooth muscle cell model, all operating on different time scales. Details of the single scale models and of the coupling interfaces are described, together with first simulation results, obtained with a dedicated software environment for Complex Automata simulations. The results show that the model can reproduce growth trends observed in experimental studies.