Virtual FFR quantified with a generalized flow model using Windkessel boundary conditions ; Application to a patient-specific coronary tree.

International audienceFractional flow reserve (FFR) has proved its efficiency in improving patients diagnosis. In this paper, we consider a 2D reconstructed left coronary tree with two artificial lesions of different degrees. We use a generalized fluid model with a Carreau law and implement the Windkessel boundary conditions at the outlets. We introduce our methodology to quantify the FFR, and lead several numerical experiments. For two different finite element meshes, we compare the FFR results for Navier Stokes versus generalized flow models, and for Windkessel versus free outlets boundary conditions. We also used mixed boundary conditions. Our results highlight the fact that free outlets boundary conditions are sensitive to the FFR sensor position. The computational FFR results show that the degree of stenosis is not enough to classify a lesion, while there is a good agreement between Navier Stokes and generalized flow model in classifying the lesions

Patient-Specific Modeling of Altered Coronary Artery Hemodynamics to Predict Morbidity in Patients with Anomalous Origin of a Coronary Artery

Anomalous aortic origin of a coronary artery (AAOCA) is a condition where a coronary artery arises from the opposite aortic sinus, often with acute angle of origin (AO). AAOCA is associated with ischemia.1 This is especially concerning when the anomalous coronary artery takes an intramural course within the aortic wall, creating the potential for distortion or compression. Unroofing surgery replaces a restrictive ostium and intramural segment with a large ostium from the appropriate sinus and aims to create a less acute AO. Although these anatomical features may alter coronary artery blood flow patterns, hemodynamic indices such as time averaged wall shear stress (TAWSS), oscillatory shear index (OSI) and fractional flow reserve (FFR) that impact a patient’s future risk for ischemia and morbidity 2–6 remain largely unexplored. We hypothesized that morphology of the anomalous coronary artery has a significant impact on local hemodynamics of AAOCA and aimed to 1) characterize hemodynamic alterations in AAOCA by patient-specific simulation of patients pre-operative and post-unroofing using advanced coronary artery boundary conditions, 2) assess the impact of AO on the severity of hemodynamic alterations, and 3) characterize the hemodynamic effect of proximal narrowing of the anomalous artery and hyperemic resistance of the downstream vasculature (HMR) on FFR. Findings from Aim 1 suggested that different flow patterns exist natively between right and left coronary arteries, a reduction in TAWSS is observed post-unroofing, and that unroofing may normalize TAWSS but with variance related to the AO. Data from Aim 2 indicated that AO alters TAWSS and OSI in simulations run from a patient-specific model with virtually rotated AOs. The arterial wall experienced lower TAWSS for more acute AO near the ostium. Distal to the ostium, arterial wall experienced higher TAWSS for more acute AO. Findings from Aim 3 showed that for a given narrowing, higher HMR resulted in higher FFR thereby mimicking the interaction of the upstream and downstream micro-vasculature resistance to regulate FFR for the first time using computational models of AAOCA. Virtual manipulation of the anomalous artery provided a direct comparison for the effect of the anatomic high-risk features. Collectively, these results serve as the foundation for larger studies of AAOCA that could correlate hemodynamics with outcomes for risk stratification and surgical evaluation

Patient-specific blood flow modelling in diagnosis of coronary artery disease

This thesis presents the approach for development of patient-specific coronary blood flow models in 3D and 0D domains based on coronary artery geometries reconstructed from Coronary Computed Tomography Angiography datasets (CCTA). The computed flow patterns extend the diagnostic value of CCTA, which, being noninvasive imaging modality, provides only static information on the anatomy of epicardial arteries. The clinical indices extracted from the virtual blood flow can be potentially employed in the assessment of the haemodynamic severity of Coronary Artery Disease (CAD) lesions as well at the analysis of the underlying mechanisms of formation and localisation of atherosclerotic plaques. However, the existing patient-specific coronary blood flow modelling approaches are generally characterised by relatively high levels of uncertainty and instability due to a number of unknown factors and modelling assumptions. Analysis and comparison of the impact of various modelling assumptions has the potential to reduce this uncertainty. The overarching contributions of this thesis are the thorough analysis and investigation of the existing issues in patient-specific coronary blood flow simulations and the provision of the guidelines for the design and implementation of blood flow models in order to improve and ensure the reliability and accuracy of the numerical results. In addition, a novel approach for the implementation of spatially extended patient-specific 0D blood flow models was proposed, which significantly decreases the high computational costs generally associated with 3D blood flow simulations. While the classical 0D models based on the electrical–hydraulic analogy use the lumped-parameter representation of major vessel tree structures and are thus characterised by limited spatial characteristics, the proposed method for modelling of individual vessel tree branches through a series of 0D elements provides the means for correlation of the computed flow with the precise location along a vessel. Therefore, this extends the applicability of 0D modelling in patient-specific blood flow simulations for the assessment of functional stenosis severity

Methods and Algorithms for Cardiovascular Hemodynamics with Applications to Noninvasive Monitoring of Proximal Blood Pressure and Cardiac Output Using Pulse Transit Time

Advanced health monitoring and diagnostics technology are essential to reduce the unrivaled number of human fatalities due to cardiovascular diseases (CVDs). Traditionally, gold standard CVD diagnosis involves direct measurements of the aortic blood pressure (central BP) and flow by cardiac catheterization, which can lead to certain complications. Understanding the inner-workings of the cardiovascular system through patient-specific cardiovascular modeling can provide new means to CVD diagnosis and relating treatment. BP and flow waves propagate back and forth from heart to the peripheral sites, while carrying information about the properties of the arterial network. Their speed of propagation, magnitude and shape are directly related to the properties of blood and arterial vasculature. Obtaining functional and anatomical information about the arteries through clinical measurements and medical imaging, the digital twin of the arterial network of interest can be generated. The latter enables prediction of BP and flow waveforms along this network. Point of care devices (POCDs) can now conduct in-home measurements of cardiovascular signals, such as electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG) and even direct measurements of the pulse transit time (PTT). This vital information provides new opportunities for designing accurate patient-specific computational models eliminating, in many cases, the need for invasive measurements. One of the main efforts in this area is the development of noninvasive cuffless BP measurement using patient’s PTT. Commonly, BP prediction is carried out with regression models assuming direct or indirect relationships between BP and PTT. However, accounting for the nonlinear FSI mechanics of the arteries and the cardiac output is indispensable. In this work, a monotonicity-preserving quasi-1D FSI modeling platform is developed, capable of capturing the hyper-viscoelastic vessel wall deformation and nonlinear blood flow dynamics in arbitrary arterial networks. Special attention has been dedicated to the correct modeling of discontinuities, such as mechanical properties mismatch associated with the stent insertion, and the intertwining dynamics of multiscale 3D and 1D models when simulating the arterial network with an aneurysm. The developed platform, titled Cardiovascular Flow ANalysis (CardioFAN), is validated against well-known numerical, in vitro and in vivo arterial network measurements showing average prediction errors of 5.2%, 2.8% and 1.6% for blood flow, lumen cross-sectional area, and BP, respectively. CardioFAN evaluates the local PTT, which enables patient-specific calibration and its application to input signal reconstruction. The calibration is performed based on BP, stroke volume and PTT measured by POCDs. The calibrated model is then used in conjunction with noninvasively measured peripheral BP and PTT to inversely restore the cardiac output, proximal BP and aortic deformation in human subjects. The reconstructed results show average RMSEs of 1.4% for systolic and 4.6% for diastolic BPs, as well as 8.4% for cardiac output. This work is the first successful attempt in implementation of deterministic cardiovascular models as add-ons to wearable and smart POCD results, enabling continuous noninvasive monitoring of cardiovascular health to facilitate CVD diagnosis

Computational methods in cardiovascular mechanics

The introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology. Together with the dramatic improvement of imaging and measuring devices on one side, and of computational architectures on the other one, mathematical and numerical models have provided a new, clearly noninvasive, approach for understanding not only basic mechanisms but also patient-specific conditions, and for supporting the design and the development of new therapeutic options. The terminology in silico is, nowadays, commonly accepted for indicating this new source of knowledge added to traditional in vitro and in vivo investigations. The advantages of in silico methodologies are basically the low cost in terms of infrastructures and facilities, the reduced invasiveness and, in general, the intrinsic predictive capabilities based on the use of mathematical models. The disadvantages are generally identified in the distance between the real cases and their virtual counterpart required by the conceptual modeling that can be detrimental for the reliability of numerical simulations.Comment: 54 pages, Book Chapte