Toward translating near-infrared spectroscopy oxygen saturation data for the non-invasive prediction of spatial and temporal hemodynamics during exercise.

Image-based computational fluid dynamics (CFD) studies conducted at rest have shown that atherosclerotic plaque in the thoracic aorta (TA) correlates with adverse wall shear stress (WSS), but there is a paucity of such data under elevated flow conditions. We developed a pedaling exercise protocol to obtain phase contrast magnetic resonance imaging (PC-MRI) blood flow measurements in the TA and brachiocephalic arteries during three-tiered supine pedaling at 130, 150, and 170 % of resting heart rate (HR), and relate these measurements to non-invasive tissue oxygen saturation [Formula: see text] acquired by near-infrared spectroscopy (NIRS) while conducting the same protocol. Local quantification of WSS indices by CFD revealed low time-averaged WSS on the outer curvature of the ascending aorta and the inner curvature of the descending aorta (dAo) that progressively increased with exercise, but that remained low on the anterior surface of brachiocephalic arteries. High oscillatory WSS observed on the inner curvature of the aorta persisted during exercise as well. Results suggest locally continuous exposure to potentially deleterious indices of WSS despite benefits of exercise. Linear relationships between flow distributions and tissue oxygen extraction calculated from [Formula: see text] were found between the left common carotid versus cerebral tissue [Formula: see text] and the dAo versus leg tissue [Formula: see text]. A resulting six-step procedure is presented to use NIRS data as a surrogate for exercise PC-MRI when setting boundary conditions for future CFD studies of the TA under simulated exercise conditions. Relationships and ensemble-averaged PC-MRI inflow waveforms are provided in an online repository for this purpose.

Image-based quantification of 3D morphology for bifurcations in the left coronary artery: Application to stent design.

BACKGROUND: Improved strategies for stent-based treatment of coronary artery disease at bifurcations require a greater understanding of artery morphology. OBJECTIVE: We developed a workflow to quantify morphology in the left main coronary (LMCA), left anterior descending (LAD), and left circumflex (LCX) artery bifurcations. METHODS: Computational models of each bifurcation were created for 55 patients using computed tomography images in 3D segmentation software. Metrics including cross-sectional area, length, eccentricity, taper, curvature, planarity, branching law parameters, and bifurcation angles were assessed using open-sources software and custom applications. Geometric characterization was performed by comparison of means, correlation, and linear discriminant analysis (LDA). RESULTS: Differences between metrics suggest dedicated or multistent approaches should be tailored for each bifurcation. For example, the side branch of the LCX (i.e., obtuse marginal; OM) was longer than that of the LMCA (i.e., LCXprox) and LAD (i.e., first diagonal; D1). Bifurcation metrics for some locations (e.g., LMCA Finet ratio) provide results and confidence intervals agreeing with prior findings, while revised metric values are presented for others (e.g., LAD and LCX). LDA revealed several metrics that differentiate between artery locations (e.g., LMCA vs. D1, LMCA vs. OM, LADprox vs. D1, and LCXprox vs. D1). CONCLUSIONS: These results provide a foundation for elucidating common parameters from healthy coronary arteries and could be leveraged in the future for treating diseased arteries. Collectively the current results may ultimately be used for design iterations that improve outcomes following implantation of future dedicated bifurcation stents. © 2015 Wiley Periodicals, Inc.

Bifurcation study of blood flow control in the kidney.

Renal blood flow is maintained within a narrow window by a set of intrinsic autoregulatory mechanisms. Here, a mathematical model of renal hemodynamics control in the rat kidney is used to understand the interactions between two major renal autoregulatory mechanisms: the myogenic response and tubuloglomerular feedback. A bifurcation analysis of the model equations is performed to assess the effects of the delay and sensitivity of the feedback system and the time constants governing the response of vessel diameter and smooth muscle tone. The results of the bifurcation analysis are verified using numerical simulations of the full nonlinear model. Both the analytical and numerical results predict the generation of limit cycle oscillations under certain physiologically relevant conditions, as observed in vivo.

Immersive visualization for enhanced computational fluid dynamics analysis.

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility.

Including aortic valve morphology in computational fluid dynamics simulations: initial findings and application to aortic coarctation.

Computational fluid dynamics (CFD) simulations quantifying thoracic aortic flow patterns have not included disturbances from the aortic valve (AoV). 80% of patients with aortic coarctation (CoA) have a bicuspid aortic valve (BAV) which may cause adverse flow patterns contributing to morbidity. Our objectives were to develop a method to account for the AoV in CFD simulations, and quantify its impact on local hemodynamics. The method developed facilitates segmentation of the AoV, spatiotemporal interpolation of segments, and anatomic positioning of segments at the CFD model inlet. The AoV was included in CFD model examples of a normal (tricuspid AoV) and a post-surgical CoA patient (BAV). Velocity, turbulent kinetic energy (TKE), time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) results were compared to equivalent simulations using a plug inlet profile. The plug inlet greatly underestimated TKE for both examples. TAWSS differences extended throughout the thoracic aorta for the CoA BAV, but were limited to the arch for the normal example. OSI differences existed mainly in the ascending aorta for both cases. The impact of AoV can now be included with CFD simulations to identify regions of deleterious hemodynamics thereby advancing simulations of the thoracic aorta one step closer to reality.

Computational simulations for aortic coarctation: representative results from a sampling of patients.

Treatments for coarctation of the aorta (CoA) can alleviate blood pressure (BP) gradients (Δ), but long-term morbidity still exists that can be explained by altered indices of hemodynamics and biomechanics. We introduce a technique to increase our understanding of these indices for CoA under resting and nonresting conditions, quantify their contribution to morbidity, and evaluate treatment options. Patient-specific computational fluid dynamics (CFD) models were created from imaging and BP data for one normal and four CoA patients (moderate native CoA: Δ12 mmHg, severe native CoA: Δ25 mmHg and postoperative end-to-end and end-to-side patients: Δ0 mmHg). Simulations incorporated vessel deformation, downstream vascular resistance and compliance. Indices including cyclic strain, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were quantified. Simulations replicated resting BP and blood flow data. BP during simulated exercise for the normal patient matched reported values. Greatest exercise-induced increases in systolic BP and mean and peak ΔBP occurred for the moderate native CoA patient (SBP: 115 to 154 mmHg; mean and peak ΔBP: 31 and 73 mmHg). Cyclic strain was elevated proximal to the coarctation for native CoA patients, but reduced throughout the aorta after treatment. A greater percentage of vessels was exposed to subnormal TAWSS or elevated OSI for CoA patients. Local patterns of these indices reported to correlate with atherosclerosis in normal patients were accentuated by CoA. These results apply CFD to a range of CoA patients for the first time and provide the foundation for future progress in this area.

Modeling cardio-respiratory system response to inhaled CO2 in patients with congestive heart failure.

In this paper we examine a cardiovascular-respiratory model of mid-level complexity designed to predict the dynamics of end-tidal carbon dioxide (CO(2)) and cerebral blood flow velocity in response to a CO(2) challenge. Respiratory problems often emerge as heart function diminishes in congestive heart failure patients. To assess system function, various tests can be performed including inhalation of a higher than normal CO(2) level. CO(2) is a key quantity firstly because any perturbation in system CO(2) quickly influences ventilation (oxygen perturbations need to be more severe). Secondly, the CO(2) response gain has been associated with respiratory system control instability. Thirdly, CO(2) in a short time impacts the degree of cerebral vascular constriction, allowing for the assessment of cerebral vasculature function. The presented model can be used to study key system characteristics including cerebral vessel CO(2) reactivity and ventilatory feedback factors influencing ventilatory stability in patients with congestive heart failure. Accurate modeling of the dynamics of system response to CO(2) challenge, in conjunction with robust parameter identification of key system parameters, can help in assessing patient system status.

Estimation and identification of parameters in a lumped cerebrovascular model.

This study shows how sensitivity analysis and subset selection can be employed in a cardiovascular model to estimate total systemic resistance, cerebrovascular resistance, arterial compliance, and time for peak systolic ventricular pressure for healthy young and elderly subjects. These quantities are parameters in a simple lumped parameter model that predicts pressure and flow in the systemic circulation. The model is combined with experimental measurements of blood flow velocity from the middle cerebral artery and arterial finger blood pressure. To estimate the model parameters we use nonlinear optimization combined with sensitivity analysis and subset selection. Sensitivity analysis allows us to rank model parameters from the most to the least sensitive with respect to the output states (cerebral blood flow velocity and arterial blood pressure). Subset selection allows us to identify a set of independent candidate parameters that can be estimated given limited data. Analyses of output from both methods allow us to identify five independent sensitive parameters that can be estimated given the data. Results show that with the advance of age total systemic and cerebral resistances increase, that time for peak systolic ventricular pressure is increases, and that arterial compliance is reduced. Thus, the method discussed in this study provides a new methodology to extract clinical markers that cannot easily be assessed noninvasively.

Sensitivity analysis and model assessment: mathematical models for arterial blood flow and blood pressure.

The complexity of mathematical models describing the cardiovascular system has grown in recent years to more accurately account for physiological dynamics. To aid in model validation and design, classical deterministic sensitivity analysis is performed on the cardiovascular model first presented by Olufsen, Tran, Ottesen, Ellwein, Lipsitz and Novak (J Appl Physiol 99(4):1523-1537, 2005). This model uses 11 differential state equations with 52 parameters to predict arterial blood flow and blood pressure. The relative sensitivity solutions of the model state equations with respect to each of the parameters is calculated and a sensitivity ranking is created for each parameter. Parameters are separated into two groups: sensitive and insensitive parameters. Small changes in sensitive parameters have a large effect on the model solution while changes in insensitive parameters have a negligible effect. This analysis was successfully used to reduce the effective parameter space by more than half and the computation time by two thirds. Additionally, a simpler model was designed that retained the necessary features of the original model but with two-thirds of the state equations and half of the model parameters.

Blood pressure and blood flow variation during postural change from sitting to standing: model development and validation.

Short-term cardiovascular responses to postural change from sitting to standing involve complex interactions between the autonomic nervous system, which regulates blood pressure, and cerebral autoregulation, which maintains cerebral perfusion. We present a mathematical model that can predict dynamic changes in beat-to-beat arterial blood pressure and middle cerebral artery blood flow velocity during postural change from sitting to standing. Our cardiovascular model utilizes 11 compartments to describe blood pressure, blood flow, compliance, and resistance in the heart and systemic circulation. To include dynamics due to the pulsatile nature of blood pressure and blood flow, resistances in the large systemic arteries are modeled using nonlinear functions of pressure. A physiologically based submodel is used to describe effects of gravity on venous blood pooling during postural change. Two types of control mechanisms are included: 1) autonomic regulation mediated by sympathetic and parasympathetic responses, which affect heart rate, cardiac contractility, resistance, and compliance, and 2) autoregulation mediated by responses to local changes in myogenic tone, metabolic demand, and CO(2) concentration, which affect cerebrovascular resistance. Finally, we formulate an inverse least-squares problem to estimate parameters and demonstrate that our mathematical model is in agreement with physiological data from a young subject during postural change from sitting to standing.