Computational models of the heart's mitral valve (MV) exhibit potential for preoperative surgical planning in ischemic mitral regurgitation (IMR). However challenges exist in defining boundary conditions to accurately model the function and response of the chordae tendineae to both IMR and surgical annuloplasty repair. Towards this goal, a ground-truth data set was generated by quantifying the isolated effects of IMR and mitral annuloplasty on leaflet coaptation, regurgitation, and tethering forces of the anterior strut and posterior intermediary chordae tendineae. MVs were excised from ovine hearts (N = 15) and mounted in a pulsatile heart simulator which has been demonstrated to mimic the systolic MV geometry and coaptation of healthy and chronic IMR sheep. Strut and intermediary chordae from both MV leaflets (N = 4) were instrumented with force transducers. Tested conditions included a healthy control, IMR, oversized annuloplasty, true-sized annuloplasty, and undersized mitral annuloplasty. A2-P2 leaflet coaptation length, regurgitation, and chordal tethering were quantified and statistically compared across experimental conditions. MR was successfully simulated with significant increases in MR, tethering forces for each of the chordae, and decrease in leaflet coaptation (p < .05). Compared to the IMR condition, increasing levels of downsized annuloplasty significantly reduced regurgitation, increased coaptation, reduced posteromedial papillary muscle strut chordal forces, and reduced intermediary chordal forces from the anterolateral papillary muscle (p < .05). These results provide for the first time a novel comprehensive data set for refining the ability of computational MV models to simulate IMR and varying sizes of complete rigid ring annuloplasty.
Over the years, three-dimensional models of the mitral valve have generally been organized around a simplified anatomy. Leaflets have been typically modeled as membranes, tethered to discrete chordae typically modeled as one-dimensional, non-linear cables. Yet, recent, high-resolution medical images have revealed that there is no clear boundary between the chordae and the leaflets. In fact, the mitral valve has been revealed to be more of a webbed structure whose architecture is continuous with the chordae and their extensions into the leaflets. Such detailed images can serve as the basis of anatomically accurate, subject-specific models, wherein the entire valve is modeled with solid elements that more faithfully represent the chordae, the leaflets, and the transition between the two. These models have the potential to enhance our understanding of mitral valve mechanics and to re-examine the role of the mitral valve chordae, which heretofore have been considered to be ‘invisible’ to the fluid and to be of secondary importance to the leaflets. However, these new models also require a rethinking of modeling assumptions. In this study, we examine the conventional practice of loading the leaflets only and not the chordae in order to study the structural response of the mitral valve apparatus. Specifically, we demonstrate that fully resolved 3D models of the mitral valve require a fluid–structure interaction analysis to correctly load the valve even in the case of quasi-static mechanics. While a fluid–structure interaction mode is still more computationally expensive than a structural-only model, we also show that advances in GPU computing have made such models tractable.
Computational fluid dynamics (CFD) tools have been extensively applied to study the hemodynamics in the total cavopulmonary connection (TCPC) in patients with only a single functioning ventricle. Without the contraction of a sub-pulmonary ventricle, pulsatility of flow through this connection is low and variable across patients, which is usually neglected in most numerical modeling studies. Recent studies suggest that such pulsatility can be non-negligible and can be important in hemodynamic predictions. The goal of this work is to compare the results of an in-house numerical methodology for simulating pulsatile TCPC flow with experimental results. Digital particle image velocimetry (DPIV) was acquired on TCPC in vitro models to evaluate the capability of the CFD tool in predicting pulsatile TCPC flow fields. In vitro hemodynamic measurements were used to compare the numerical prediction of power loss across the connection. The results demonstrated the complexity of the pulsatile TCPC flow fields and the validity of the numerical approach in simulating pulsatile TCPC flow dynamics in both idealized and complex patient specific models.
Total cavopulmonary connection is the result of a series of palliative surgical repairs performed on patients with single ventricle heart defects. The resulting anatomy has complex and unsteady hemodynamics characterized by flow mixing and flow separation. Although varying degrees of flow pulsatility have been observed in vivo, non-pulsatile (time-averaged) boundary conditions have traditionally been assumed in hemodynamic modeling, and only recently have pulsatile conditions been incorporated without completely characterizing their effect or importance. In this study, 3D numerical simulations with both pulsatile and non-pulsatile boundary conditions were performed for 24 patients with different anatomies and flow boundary conditions from Georgia Tech database. Flow structures, energy dissipation rates and pressure drops were compared under rest and simulated exercise conditions. It was found that flow pulsatility is the primary factor in determining the appropriate choice of boundary conditions, whereas the anatomic configuration and cardiac output had secondary effects. Results show that the hemodynamics can be strongly influenced by the presence of pulsatile flow. However, there was a minimum pulsatility threshold, identified by defining a weighted pulsatility index (wPI), above which the influence was significant. It was shown that when wPI < 30%, the relative error in hemodynamic predictions using time-averaged boundary conditions was less than 10% compared to pulsatile simulations. In addition, when wPI < 50, the relative error was less than 20%. A correlation was introduced to relate wPI to the relative error in predicting the flow metrics with non-pulsatile flow conditions.
To conduct a patient-specific computational modeling of the aortic valve, 3-D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3-D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual finite element (FE) model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results but also allow fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3-D geometries of the aortic valve from computed tomographic images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from 10 patients with those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to middiastole was simulated for 7 patients and validated by comparing the deformed geometries with those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a preoperative planning system for aortic valve disease diagnosis and treatment.
Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement.
This study aims to investigate the capability of smoothed particle hydrodynamics (SPH), a fully Lagrangian mesh-free method, to simulate the bulk blood flow dynamics in two realistic left ventricular (LV) models. Three dimensional geometries and motion of the LV, proximal left atrium and aortic root are extracted from cardiac magnetic resonance imaging and multi-slice computed tomography imaging data. SPH simulation results are analyzed and compared with those obtained using a traditional finite volume-based numerical method, and to in vivo phase contrast magnetic resonance imaging and echocardiography data, in terms of the large-scale blood flow phenomena usually clinically measured. A quantitative comparison of the velocity fields and global flow parameters between the in silico models and the in vivo data shows a reasonable agreement, given the inherent uncertainties and limitations in the modeling and imaging techniques. The results indicate the capability of SPH as a promising tool for predicting clinically relevant large-scale LV flow information.
Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.
Stem Cells Translational Medicine published by Wiley Periodicals, Inc. on behalf of AlphaMed Press Congenital heart disease can lead to severe right ventricular heart failure (RVHF). We have shown that aggregated c-kit+ progenitor cells (CPCs) can improve RVHF repair, likely due to exosome-mediated effects. Here, we demonstrate that miRNA content from monolayer (2D) and aggregated (3D) CPC exosomes can be related to in vitro angiogenesis and antifibrosis responses using partial least squares regression (PLSR). PLSR reduced the dimensionality of the data set to the top 40 miRNAs with the highest weighted coefficients for the in vitro biological responses. Target pathway analysis of these top 40 miRNAs demonstrated significant fit to cardiac angiogenesis and fibrosis pathways. Although the model was trained on in vitro data, we demonstrate that the model can predict angiogenesis and fibrosis responses to exosome treatment in vivo with a strong correlation with published in vivo responses. These studies demonstrate that PLSR modeling of exosome miRNA content has the potential to inform preclinical trials and predict new promising CPC therapies. Stem Cells Translational Medicine 2019;8:1212–1221.
With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI).
All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73–90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s−1. The thresholds compare favorably with tissue tolerances found in in–vitro/in–vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05–4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02–1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11–41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.