Abnormal Tissue Zone Detection and Average Active Stress Estimation in Patients with LV Dysfunction

Detection of regional ventricular dysfunction is a challenging problem. This study presents an efficient method based on ultrasound (US) imaging and finite element (FE) analysis, for detecting akinetic and dyskinetic regions in the left ventricle (LV). The underlying hypothesis is that the contraction of a healthy LV is approximately homogeneous. Therefore, any deviations between the image-based measured deformation and a homogeneous contraction FE model should correspond to a pathological region. The method was first successfully applied to synthetic data simulating an acute ischemia; it demonstrated that the pathological areas were revealed with a higher contrast than those observed directly in the deformation maps. The technique was then applied to a cohort of eight left bundle branch block (LBBB) patients. For this group, the heterogeneities were significantly less pronounced than those revealed for the synthetic cases but the method was still able to identify the abnormal regions of the LV. This study indicated the potential clinical utility of the method by its simplicity in a patient-specific context and its ability to quickly identify various heterogeneities in LV function. Further studies are required to determine the model accuracy in other pathologies and to investigate its robustness to noise and image artifacts

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

Shape analysis in shape space

This study aims to classify different deformations based on the shape space concept. A shape space is a quotient space in which each point corresponds to a class of shapes. The shapes of each class are transformed to each other by a transformation group preserving a geometrical property in which we are interested. Therefore, each deformation is a curve on the high dimensional shape space manifold, and one can classify the deformations by comparison of their corresponding deformation curves in shape space. Towards this end, two classification methods are proposed. In the first method, a quasi conformal shape space is constructed based on a novel quasi-conformal metric, which preserves the curvature changes at each vertex during the deformation. Besides, a classification framework is introduced for deformation classification. The results on synthetic and real datasets show the effectiveness of the metric to estimate the intrinsic geometry of the shape space manifold, and its ability to classify and interpolate different deformations. In the second method, we introduce the medial surface shape space which classifies the deformations based on the medial surface and thickness of the shape. This shape space is based on the log map and uses two novel measures, average of the normal vectors and mean of the positions, to determine the distance between each pair of shapes on shape space. We applied these methods to classify the left ventricle deformations. The experimental results shows that the first method can remarkably classify the normal and abnormal subjects but this method cannot spot the location of the abnormality. In contrast, the second method can discriminate healthy subjects from patients with cardiomyopathy, and also can spot the abnormality on the left ventricle, which makes it a valuable assistant tool for diagnostic purposes

Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta

Integrated Heart - Coupling multiscale and multiphysics models for the simulation of the cardiac function

Mathematical modelling of the human heart and its function can expand our understanding of various cardiac diseases, which remain the most common cause of death in the developed world. Like other physiological systems, the heart can be understood as a complex multiscale system involving interacting phenomena at the molecular, cellular, tissue, and organ levels. This article addresses the numerical modelling of many aspects of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle tissue, the sub-cellular activation-contraction mechanisms, as well as the hemodynamics inside the heart chambers. Resolution of each of these sub-systems requires separate mathematical analysis and specially developed numerical algorithms, which we review in detail. By using specific sub-systems as examples, we also look at systemic stability, and explain for example how physiological concepts such as microscopic force generation in cardiac muscle cells, translate to coupled systems of differential equations, and how their stability properties influence the choice of numerical coupling algorithms. Several numerical examples illustrate three fundamental challenges of developing multiphysics and multiscale numerical models for simulating heart function, namely: (i) the correct upscaling from single-cell models to the entire cardiac muscle, (ii) the proper coupling of electrophysiology and tissue mechanics to simulate electromechanical feedback, and (iii) the stable simulation of ventricular hemodynamics during rapid valve opening and closure