Showing papers in "International Journal for Numerical Methods in Biomedical Engineering in 2019"

Diagnosis of Alzheimer's disease with Sobolev gradient-based optimization and 3D convolutional neural network.

Abstract: Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor-based and magnetic resonance-based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient-based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy.

A rule‐based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Abstract: Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefrontpropagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.

Variational data assimilation for transient blood flow simulations - Cerebral aneurysms as an illustrative example

Abstract: Several cardiovascular diseases are caused from localised abnormal blood flow such as in the case of stenosis or aneurysms. Prevailing theories propose that the development is caused by abnormal wall shear stress in focused areas. Computational fluid mechanics have arisen as a promising tool for a more precise and quantitative analysis, in particular because the anatomy is often readily available even by standard imaging techniques such as magnetic resonance and computed tomography angiography. However, computational fluid mechanics rely on accurate initial and boundary conditions, which are difficult to obtain. In this paper, we address the problem of recovering high-resolution information from noisy and low-resolution physical measurements of blood flow (for example, from phase-contrast magnetic resonance imaging [PC-MRI]) using variational data assimilation based on a transient Navier-Stokes model. Numerical experiments are performed in both 3D (2D space and time) and 4D (3D space and time) and with pulsatile flow relevant for physiological flow in cerebral aneurysms. The results demonstrate that, with suitable regularisation, the model accurately reconstructs flow, even in the presence of significant noise.

A semi‐active human digital twin model for detecting severity of carotid stenoses from head vibration—A coupled computational mechanics and computer vision method

Abstract: In this work, we propose a methodology to detect the severity of carotid stenosis from a video of a human face with the help of a coupled blood flow and head vibration model. This semi-active digital twin model is an attempt to link noninvasive video of a patient face to the percentage of carotid occlusion. The pulsatile nature of blood flow through the carotid arteries induces a subtle head vibration. This vibration is a potential indicator of carotid stenosis severity, and it is exploited in the present study. A head vibration model has been proposed in the present work that is linked to the forces generated by blood flow with or without occlusion. The model is used to generate a large number of virtual head vibration data for different degrees of occlusion. In order to determine the in vivo head vibration, a computer vision algorithm is adopted to use human face videos. The in vivo vibrations are compared against the virtual vibration data generated from the coupled computational blood flow/vibration model. A comparison of the in vivo vibration is made against the virtual data to find the best fit between in vivo and virtual data. The preliminary results on healthy subjects and a patient clearly indicate that the model is accurate and it possesses the potential for detecting approximate severity of carotid artery stenoses.

A computational model for microcirculation including Fahraeus-Lindqvist effect, plasma skimming and fluid exchange with the tissue interstitium.

Abstract: We present a two-phase model for microcirculation that describes the interaction of plasma with red blood cells. The model takes into account of typical effects characterizing the microcirculation, such as the Fahraeus-Lindqvist effect and plasma skimming. Besides these features, the model describes the interaction of capillaries with the surrounding tissue. More precisely, the model accounts for the interaction of capillary transmural flow with the surrounding interstitial pressure. Furthermore, the capillaries are represented as one-dimensional channels with arbitrary, possibly curved configuration. The latter two features rely on the unique ability of the model to account for variations of flow rate and pressure along the axis of the capillary, according to a local differential formulation of mass and momentum conservation. Indeed, the model stands on a solid mathematical foundation, which is also addressed in this work. In particular, we present the model derivation, the variational formulation, and its approximation using the finite element method. Finally, we conclude the work with a comparative computational study of the importance of the Fahraeus-Lindqvist, plasma skimming, and capillary leakage effects on the distribution of flow in a microvascular network.

DG-GL: Differential geometry-based geometric learning of molecular datasets.

Abstract: Motivation Despite its great success in various physical modeling, differential geometry (DG) has rarely been devised as a versatile tool for analyzing large, diverse, and complex molecular and biomolecular datasets because of the limited understanding of its potential power in dimensionality reduction and its ability to encode essential chemical and biological information in differentiable manifolds. Results We put forward a differential geometry-based geometric learning (DG-GL) hypothesis that the intrinsic physics of three-dimensional (3D) molecular structures lies on a family of low-dimensional manifolds embedded in a high-dimensional data space. We encode crucial chemical, physical, and biological information into 2D element interactive manifolds, extracted from a high-dimensional structural data space via a multiscale discrete-to-continuum mapping using differentiable density estimators. Differential geometry apparatuses are utilized to construct element interactive curvatures in analytical forms for certain analytically differentiable density estimators. These low-dimensional differential geometry representations are paired with a robust machine learning algorithm to showcase their descriptive and predictive powers for large, diverse, and complex molecular and biomolecular datasets. Extensive numerical experiments are carried out to demonstrate that the proposed DG-GL strategy outperforms other advanced methods in the predictions of drug discovery-related protein-ligand binding affinity, drug toxicity, and molecular solvation free energy. Availability and implementation http://weilab.math.msu.edu/DG-GL/ Contact: wei@math.msu.edu.

Analysis of a coupled fluid-structure interaction model of the left atrium and mitral valve.

Abstract: We present a coupled left atrium ‐ mitral valve model based on computed tomography scans with fibre‐reinforced hyperelastic materials. Fluid‐structure interaction is realised by using an immersed boundary‐finite element framework. Effects of pathological conditions, e.g. mitral valve regurgitation and atrial fibrillation, and geometric and structural variations, namely uniform vs non‐uniform atrial wall thickness and rule‐based vs atlas‐based fibre architectures, on the system are investigated. We show that in the case of atrial fibrillation, pulmonary venous flow reversal at late diastole disappears and the filling waves at the left atrial appendage orifice during systole have reduced magnitude. In the case of mitral regurgitation, a higher atrial pressure and disturbed flows are seen, especially during systole, when a large regurgitant jet can be found with the suppressed pulmonary venous flow. We also show that both the rule‐based and atlas‐based fibre defining methods lead to similar flow fields and atrial wall deformations. However, the changes in wall thickness from non‐uniform to uniform tend to underestimate the atrial deformation. Using a uniform but thickened wall also lowers the overall strain level. The flow velocity within the left atrial appendage, which is important in terms of appendage thrombosis, increases with the thickness of the left atrial wall. Energy analysis shows that the kinetic and dissipation energies of the flow within the left atrium are altered differently by atrial fibrillation and mitral valve regurgitation, providing a useful indication of the atrial performance in pathological situations.

Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies

Abstract: Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced‐order modelling and one based on a 3D rigid‐wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced‐order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced‐order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced‐order model did not include a lumped pressure‐drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure‐drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online.

The FDA nozzle benchmark: "In theory there is no difference between theory and practice, but in practice there is".

Abstract: The utility of flow simulations relies on the robustness of computational fluid dynamics (CFD) solvers and reproducibility of results The aim of this study was to validate the Oasis CFD solver against in vitro experimental measurements of jet breakdown location from the FDA nozzle benchmark at Reynolds number 3500, which is in the particularly challenging transitional regime Simulations were performed on meshes consisting of 5, 10, 17, and 28 million (M) tetrahedra, with Δt = 10-5 seconds The 5M and 10M simulation jets broke down in reasonable agreement with the experiments However, the 17M and 28M simulation jets broke down further downstream But which of our simulations are "correct"? From a theoretical point of view, they are all wrong because the jet should not break down in the absence of disturbances The geometry is axisymmetric with no geometrical features that can generate angular velocities A stable flow was supported by linear stability analysis From a physical point of view, a finite amount of "noise" will always be present in experiments, which lowers transition point To replicate noise numerically, we prescribed minor random angular velocities (approximately 031%), much smaller than the reported flow asymmetry (approximately 3%) and model accuracy (approximately 1%), at the inlet of the 17M simulation, which shifted the jet breakdown location closer to the measurements Hence, the high-resolution simulations and "noise" experiment can potentially explain discrepancies in transition between sometimes "sterile" CFD and inherently noisy "ground truth" experiments Thus, we have shown that numerical simulations can agree with experiments, but for the wrong reasons

Bone fracture healing under Ilizarov fixator: Influence of fixator configuration, fracture geometry, and loading.

Abstract: This study aims to enhance the understanding of the relationship between Ilizarov fixator configuration and its effects on bone fracture healing. Using Taylor spatial frame (TSF) as an example, the roles of critical parameters (ie, TSF ring diameter, wire pre-tension, fracture gap size, and axial load) that govern fracture healing during the early stages were investigated by using computational modelling in conjunction with mechanical testing involving an advanced 3D optical measurement system. The computational model was first validated using the mechanical test results and then used to simulate mesenchymal stem cell (MSC) differentiations within different regions of the fracture site under various combinations of TSF ring diameter, wire pre-tension, fracture gap size, and axial load values. Predicted spatially dependent MSC differentiation patterns and the influence of each parameter on differentiations were compared with in vivo results, and good agreement was seen between the two. Gap size was identified as the most influential parameter in MSC differentiation, and the influence of axial loading and TSF configuration (ie, ring diameter and wire pre-tension) on cell differentiation was seen to be gap size dependent. Most changes in cell differentiation were predicted in the external callus (periosteal), which is the crucial region of the callus in the early stages. However, for small gap sizes (eg, 1 mm), significant changes were predicted in the endosteal callus as well. The study exhibits the potential of computational models in assessing the performance of Ilizarov fixators as well as assisting surgeons in patient-specific clinical treatment planning.

Ultrafine particle deposition in a realistic human airway at multiple inhalation scenarios.

Abstract: The scarcity of regional deposition data in distal respiratory airways represents an important challenge for current toxicology and pharmacology research. To bridge this gap, a realistic airway model extending from nasal and oral openings to distal bronchial airways with varying pathway length was built in this study. Transport and deposition characteristics of naturally inhaled ultrafine particles (UFPs) ranging from 1 to 100 nm were numerically investigated, and effects of different inhalation scenarios were considered. To enable intercase particle deposition comparison, an adjusted parameter, unified deposition enhancement factor (UDEF), was proposed for quantifying the localised deposition concentration. Results show that compartment particle deposition peaked around the ultrafine end of the considered size range, and it dropped rapidly with the increase of particle size. Different inhalation modes caused notable deposition changes in the extrathoracic region, while its effects in the TB airway are much less. For UFPs larger than 10 nm, predicted deposition efficiencies in all compartments are all at lowest levels among considered particle size range, implying UFPs ranging from 10 to 100 nm can travel through the whole respiratory airway model and escape to the alveolar region. Furthermore, high enhancement factors were observed at the vicinity of most bifurcation apexes, and more even UDEF distribution was observed from 1-nm particle cases. While for 100-nm cases, the deposited particles tend to concentrate at few "hot spots" (areas of high deposition concentration in relation to surrounding surfaces) with greater UDEF in the tracheobronchial airway.

An approach for vascular tumor growth based on a hybrid embedded/homogenized treatment of the vasculature within a multiphase porous medium model.

Abstract: The aim of this work is to develop a novel computational approach to facilitate the modeling of angiogenesis during tumor growth. The preexisting vasculature is modeled as a 1D inclusion and embedded into the 3D tissue through a suitable coupling method, which allows for nonmatching meshes in 1D and 3D domain. The neovasculature, which is formed during angiogenesis, is represented in a homogenized way as a phase in our multiphase porous medium system. This splitting of models is motivated by the highly complex morphology, physiology, and flow patterns in the neovasculature, which are challenging and computationally expensive to resolve with a discrete, 1D angiogenesis and blood flow model. Moreover, it is questionable if a discrete representation generates any useful additional insight. By contrast, our model may be classified as a hybrid vascular multiphase tumor growth model in the sense that a discrete, 1D representation of the preexisting vasculature is coupled with a continuum model describing angiogenesis. It is based on an originally avascular model which has been derived via the thermodynamically constrained averaging theory. The new model enables us to study mass transport from the preexisting vasculature into the neovasculature and tumor tissue. We show by means of several illustrative examples that it is indeed capable of reproducing important aspects of vascular tumor growth phenomenologically.

Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response.

Abstract: Computational cardiac modelling is a mature area of biomedical computing and is currently evolving from a pure research tool to aiding in clinical decision making. Assessing the reliability of computational model predictions is a key factor for clinical use, and uncertainty quantification (UQ) and sensitivity analysis are important parts of such an assessment. In this study, we apply UQ in computational heart mechanics to study uncertainty both in material parameters characterizing global myocardial stiffness and in the local muscle fiber orientation that governs tissue anisotropy. The uncertainty analysis is performed using the polynomial chaos expansion (PCE) method, which is a nonintrusive meta-modeling technique that surrogates the original computational model with a series of orthonormal polynomials over the random input parameter space. In addition, in order to study variability in the muscle fiber architecture, we model the uncertainty in orientation of the fiber field as an approximated random field using a truncated Karhunen-Loeve expansion. The results from the UQ and sensitivity analysis identify clear differences in the impact of various material parameters on global output quantities. Furthermore, our analysis of random field variations in the fiber architecture demonstrate a substantial impact of fiber angle variations on the selected outputs, highlighting the need for accurate assignment of fiber orientation in computational heart mechanics models.

A novel phase field method for modeling the fracture of long bones

Abstract: A proximal humerus fracture is an injury to the shoulder joint that necessitates medical attention. While it is one of the most common fracture injuries impacting the elder community and those who suffer from traumatic falls or forceful collisions, there are almost no validated computational methods that can accurately model these fractures. This could be due to the complex, inhomogeneous bone microstructure, complex geometries, and the limitations of current fracture mechanics methods. In this paper, we develop a novel phase field method to investigate the proximal humerus fracture. To model the fracture in the inhomogeneous domain, we propose a power-law relationship between bone mineral density and critical energy release rate. The method is validated by an in vitro experiment, in which a human humerus is constrained on both ends while subjected to compressive loads on its head, in the longitudinal direction, that lead to fracture at the anatomical neck. CT scans are employed to acquire the bone geometry and material parameters, from which detailed finite element meshes with inhomogeneous Young modulus distributions are generated. The numerical method, implemented in a high performance computing environment, is used to quantitatively predict the complex 3D brittle fracture of the bone and is shown to be in good agreement with experimental observations. Furthermore, our findings show that the damage is initiated in the trabecular bone-head and propagates outward towards the bone cortex. We conclude that the proposed phase field method is a promising approach to model bone fracture.

Kinematic boundary conditions substantially impact in silico ventricular function

Abstract: Computational cardiac mechanical models, individualized to the patient, have the potential to elucidate the fundamentals of cardiac (patho-)physiology, enable non-invasive quantification of clinically significant metrics (eg, stiffness, active contraction, work), and anticipate the potential efficacy of therapeutic cardiovascular intervention. In a clinical setting, however, the available imaging resolution is often limited, which limits cardiac models to focus on the ventricles, without including the atria, valves, and proximal arteries and veins. In such models, the absence of surrounding structures needs to be accounted for by imposing realistic kinematic boundary conditions, which, for prognostic purposes, are preferably generic and thus non-image derived. Unfortunately, the literature on cardiac models shows no consistent approach to kinematically constrain the myocardium. The impact of different approaches (eg, fully constrained base, constrained epi-ring) on the predictive capacity of cardiac mechanical models has not been thoroughly studied. For that reason, this study first gives an overview of current approaches to kinematically constrain (bi) ventricular models. Next, we developed a patient-specific in silico biventricular model that compares well with literature and in vivo recorded strains. Alternative constraints were introduced to assess the influence of commonly used mechanical boundary conditions on both the predicted global functional behavior of the in-silico heart (cavity volumes, stroke volume, ejection fraction) and local strain distributions. Meaningful differences in global functioning were found between different kinematic anchoring strategies, which brought forward the importance of selecting appropriate boundary conditions for biventricular models that, in the near future, may inform clinical intervention. However, whilst statistically significant differences were also found in local strain distributions, these differences were minor and mostly confined to the region close to the applied boundary conditions.

Biomechanical modeling and computer simulation of the brain during neurosurgery.

Abstract: Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.

Simulation of unsteady blood flows in a patient-specific compliant pulmonary artery with a highly parallel monolithically coupled fluid-structure interaction algorithm.

Abstract: Computational fluid dynamics (CFD) is increasingly used to study blood flows in patient-specific arteries for understanding certain cardiovascular diseases. The techniques work quite well for relatively simple problems but need improvements when the problems become harder when (a) the geometry becomes complex (eg, a few branches to a full pulmonary artery), (b) the model becomes more complex (eg, fluid-only to coupled fluid-structure interaction), (c) both the fluid and wall models become highly nonlinear, and (d) the computer on which we run the simulation is a supercomputer with tens of thousands of processor cores. To push the limit of CFD in all four fronts, in this paper, we develop and study a highly parallel algorithm for solving a monolithically coupled fluid-structure system for the modeling of the interaction of the blood flow and the arterial wall. As a case study, we consider a patient-specific, full size pulmonary artery obtained from computed tomography (CT) images, with an artificially added layer of wall with a fixed thickness. The fluid is modeled with a system of incompressible Navier-Stokes equations, and the wall is modeled by a geometrically nonlinear elasticity equation. As far as we know, this is the first time the unsteady blood flow in a full pulmonary artery is simulated without assuming a rigid wall. The proposed numerical algorithm and software scale well beyond 10 000 processor cores on a supercomputer for solving the fluid-structure interaction problem discretized with a stabilized finite element method in space and an implicit scheme in time involving hundreds of millions of unknowns.

Sensitivity analysis for the mechanics of tendons and ligaments: Investigation on the effects of collagen structural properties via a multiscale modeling approach

Abstract: The effects of the stochasticity of collagen-related structural properties on the biomechanical properties of tendons and ligaments are investigated in this study. The tissue mechanics is modeled by means of a macroscale constitutive model based on a multiscale structural approach. This rationale allows to introduce model parameters directly associated with tissue structural and biochemical features, opening to physically motivated parametric studies. Variance and density-based global sensitivity analyses are employed, together with the quantification of output uncertainty due to stochastic variations of parameters. Novel insights on tissue structure-mechanics relationship are provided, quantifying the dependence between mechanical output quantities on specific collagen-related structural features. Moreover, the uncertainty quantification shows that model predictions provided by the multiscale structural approach are reliable with respect to inevitable uncertainties in tissue structure. Addressing rat tail tendons, the use of average values in tissue properties returns a constitutive response that fits well-available experimental data, and it is robust with respect to parameter stochasticity.

A finite difference method with subsampling for immersed boundary simulations of the capsule dynamics with viscoelastic membranes.

Abstract: The membrane or interfacial viscosity is an important property in many multiphase and biofluidic situations, such as the red blood cell dynamics and emulsion stability. The immersed boundary method (IBM), which incorporates the dynamic flow-membrane interaction via force distribution and velocity interpolation, has been extensively employed in simulations of such systems. Unfortunately, direct implementation of membrane viscosity in IBM suffers severe numerical instability, which causes an IBM calculation to break down before generating any useful results. Few attempts have been recently reported; however, several concerns exist in these attempts, such as the inconsistency to the classical definition of membrane viscosity, the inability to model the shear and dilatational viscosities separately, the unjustified mathematical formulations, and the complicated algorithms and computation. To overcome these concerns, in this paper, we propose a finite difference approach for implementing membrane viscosity in immersed boundary simulations. The viscous stress is obtained via finite difference approximations to the differential strain-stress relationship, with the help of a subsampling scheme to reduce the numerical noise in the calculated strain rates. This simple method has also avoided the complicated matrix calculations in previous attempts, and hence, a better computational efficiency is expected. Detailed mathematical description of the method and key steps for its implementation in immersed boundary programs are provided. Validation and illustration calculations are performed, and our results are compared with analytical solutions and previous publications with satisfactory agreement. The influences of membrane mesh resolution and simulation time step are also examined; and the results show no indication that our finite difference method has downgraded the general IBM accuracy. Based on these simulations and analysis, we believe that our method would be a better choice for future IBM simulations of capsule dynamics with viscoelastic membranes.

The extraction method of tooth preparation margin line based on S-Octree CNN.

Abstract: The tooth preparation margin line has a significant impact on the marginal fitness for dental restoration. Among the previous methods, the extraction of margin line mainly relies on manual interaction, which is complicated and inefficient. Therefore, we propose a method to extract the margin line with the convolutional neural network based on sparse octree (S-Octree) structure. First, the dental preparations are rotated to augment the dataset. Second, the preparation models are treated as the sparse point cloud with labels through the spatial partition method of the S-Octree. Then, based on the feature line, the dental preparation point cloud is automatically divided into two regions by the convolutional neural network (CNN). Third, in order to obtain the margin line, we adopt some methods such as the dense condition random field (dense CRF), point cloud reconstruction, and back projection to the original dental preparation model. Finally, based on the measurement indicators of accuracy, sensitivity, and specificity, the average accuracy of the label predicted by the network model can reach 97.43%. The experimental results show that our method can automatically accomplish the extraction of the tooth preparation margin line.

Computational aeroacoustics to identify sound sources in the generation of sibilant /s/.

Abstract: A sibilant fricative /s/ is generated when the turbulent jet in the narrow channel between the tongue blade and the hard palate is deflected downwards through the space between the upper and lower incisors and then impinges the space between the lower incisors and the lower lip The flow eddies in that region become a source of direct aerodynamic sound, which is also diffracted by the speech articulators and radiated outwards The numerical simulation of these phenomena is complex The spectrum of an /s/ typically peaks between 4 and 10 kHz, which implies that very fine computational meshes are needed to capture the eddies producing such high frequencies In this work, a large-scale computation of the aeroacoustics of /s/ has been performed for a realistic vocal tract geometry, resorting to two different acoustic analogies A stabilized finite element method that acts as a large eddy simulation model has been adopted to solve the flow dynamics Also, a numerical strategy has been implemented that allows the determination, in a single computational run, of the separate contribution of the sound diffracted by the upper incisors from the overall radiated sound Results are presented for points located close to the lip opening showing the relative influence of the upper teeth depending on frequency

Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models.

Abstract: In this work, we estimate the diagnostic threshold of the instantaneous wave-free ratio (iFR) through the use of a one-dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical measurements. The framework shows excellent promise and utilises minimal patient data from a cohort of 52 patients with a total of 66 stenoses. The diagnostic accuracy of the cFFR model was 75.76%, with a sensitivity of 71.43%, a specificity of 77.78%, a positive predictive value of 60%, and a negative predictive value of 85.37%. The validated model was then used to estimate the diagnostic threshold of iFR. The model determined a quadratic relationship between cFFR and the ciFR. The iFR diagnostic threshold was determined to be 0.8910 from a receiver operating characteristic curve that is in the range of 0.89 to 0.9 that is normally reported in clinical studies.

Subject-specific loads on the lumbar spine in detailed finite element models scaled geometrically and kinematic-driven by radiography images.

Abstract: Traditional load-control musculoskeletal and finite element (FE) models of the spine fail to accurately predict in vivo intervertebral joint loads due mainly to the simplifications and assumptions when estimating redundant trunk muscle forces. An alternative powerful protocol that bypasses the calculation of muscle forces is to drive the detailed FE models by image-based in vivo displacements. Development of subject-specific models, however, both involves the risk of extensive radiation exposures while imaging in supine and upright postures and is time consuming in terms of the reconstruction of the vertebrae, discs, ligaments, and facets geometries. This study therefore aimed to introduce a remedy for the development of subject-specific FE models by scaling the geometry of an existing detailed FE model of the T12-S1 lumbar spine. Five subject-specific scaled models were driven by their own radiography image-based displacements in order to predict joint loads, ligament forces, facet joint forces, and disc fiber strains during relaxed upright as well as moderate flexion and extension tasks. The predicted intradiscal pressures were found in adequate agreement with in vivo data for upright, flexion, and extension tasks. There were however large intersubject variations in the estimated joint loads and facet forces.

Computational modelling of magnesium stent mechanical performance in a remodelling artery: Effects of multiple remodelling stimuli.

Abstract: Significant research has been conducted in the area of coronary stents/scaffolds made from resorbable metallic and polymeric biomaterials. These next-generation bioabsorbable stents have the potential to completely revolutionise the treatment of coronary artery disease. The primary advantage of resorbable devices over permanent stents is their temporary presence which, from a theoretical point of view, means only a healed coronary artery will be left behind following degradation of the stent potentially eliminating long-term clinical problems associated with permanent stents. The healing of the artery following coronary stent/scaffold implantation is crucial for the long-term safety of these devices. Computational modelling can be used to evaluate the performance of complex stent devices in silico and assist in the design and development and understanding of the next-generation resorbable stents. What is lacking in computational modelling literature is the representation of the active response of the arterial tissue in the weeks and months following stent implantation, ie, neointimal remodelling, in particular for the case of biodegradable stents. In this paper, a computational modelling framework is developed, which accounts for two major physiological stimuli responsible for neointimal remodelling and combined with a magnesium corrosion model that is capable of simulating localised pitting (realistic) stent corrosion. The framework is used to simulate different neointimal growth patterns and to explore the effects the neointimal remodelling has on the mechanical performance (scaffolding support) of the bioabsorbable magnesium stent.

An in silico twin for epicardial augmentation of the failing heart.

Abstract: Advances in ventricular assist device (VAD) technology for the treatment of end-stage congestive heart failure (CHF) are needed to cope with the increasing numbers of patients that cannot be provided with donor hearts for transplantation. We develop and investigate a novel extravascular VAD technology that provides biventricular, epicardial pressure support for the failing heart. This novel VAD concept avoids blood contact that is accompanied with typical complications such as coagulation and infections. To date, in vivo porcine model results with a prototype of the implant exist, further studies to improve the implant's performance and promote its applicability in humans are needed. In this contribution, we present a personalised functional digital twin of the heart, the vascular system, and the novel VAD technology in terms of a calibrated, customized computational model. The calibration procedure is based on patient-specific measurements and is performed by solving an inverse problem. This in silico model is able to (a) confirm in vivo experimental data, (b) predict healthy and pathologic ventricular function, and (c) assess the beneficial impact of the novel VAD concept to a high level of fidelity. The model shows very good agreement with in vivo data and reliably predicts increases in stroke volume and left ventricular pressure with increasing ventricular support. Furthermore, the digital twin allows insight into quantities that are poorly or not at all amenable in any experimental setup. Conclusively, the model's ability to link integral hemodynamic variables to local tissue mechanical deformation makes it a highly valuable tool for the dimensioning of novel VAD technologies and future treatment strategies in heart failure. The presented in silico twin enhances in vivo studies by facilitating the accessibility and increasing the range of quantities of interest. Because of its flexibility in the assessment of design variants and optimization loops, it may substantially contribute to a reduction of the amount of animal experiments in this and similar settings.

Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image-derived vasculature.

Abstract: The lattice Boltzmann method (LBM) is a popular alternative to solving the Navier-Stokes equations for modeling blood flow. When simulating flow using the LBM, several choices for inlet and outlet boundary conditions exist. While boundary conditions in the LBM have been evaluated in idealized geometries, there have been no extensive comparisons in image-derived vasculature, where the geometries are highly complex. In this study, the Zou-He (ZH) and finite difference (FD) boundary conditions were evaluated in image-derived vascular geometries by comparing their stability, accuracy, and run times. The boundary conditions were compared in four arteries: a coarctation of the aorta, dissected aorta, femoral artery, and left coronary artery. The FD boundary condition was more stable than ZH in all four geometries. In general, simulations using the ZH and FD method showed similar convergence rates within each geometry. However, the ZH method proved to be slightly more accurate compared with experimental flow using three-dimensional printed vasculature. The total run times necessary for simulations using the ZH boundary condition were significantly higher as the ZH method required a larger relaxation time, grid resolution, and number of time steps for a simulation representing the same physiological time. Finally, a new inlet velocity profile algorithm is presented for complex inlet geometries. Overall, results indicated that the FD method should generally be used for large-scale blood flow simulations in image-derived vasculature geometries. This study can serve as a guide to researchers interested in using the LBM to simulate blood flow.

Finite element modeling of near-wall mass transport in cardiovascular flows.

Abstract: Many cardiovascular processes involve mass transport between blood and the vessel wall Finite element methods are commonly used to numerically simulate these processes Cardiovascular mass transport problems are typically characterized by high Peclet numbers, requiring fine near-wall mesh resolution as well as the use of stabilization techniques to avoid numerical instabilities In this work, we develop a set of guidelines for solving high-Peclet-number near-wall mass transport problems using the finite element method We use a steady, idealized test case to investigate the required mesh resolution and finite element basis order to accurately capture near-wall concentration boundary layers, as well as the performance of several commonly used stabilization techniques Linear tetrahedral meshes were found to outperform quadratic tetrahedral meshes of equivalent degrees of freedom, and the commonly used discontinuity-capturing stabilization technique was found to be overly diffusive for these types of problems Best practices derived from the idealized test case were then applied to a typical patient-specific vascular blood flow modeling application, where it was found that the commonly applied technique of avoiding numerical difficulties by artificially increasing mass diffusivity provides qualitatively similar but quantitatively erroneous results

Modern discontinuous Galerkin methods for the simulation of transitional and turbulent flows in biomedical engineering: A comprehensive LES study of the FDA benchmark nozzle model

Abstract: This work uses high-order discontinuous Galerkin discretization techniques to simulate transitional and turbulent flows through medical devices. Flows through medical devices are characterized by moderate Reynolds numbers and typically involve different flow regimes such as laminar, transitional, and turbulent flows. Previous studies for the FDA benchmark nozzle model revealed limitations of Reynolds-averaged Navier-Stokes turbulence models when applied to more complex flow scenarios. Recent works based on large-eddy simulation approaches indicate that these limitations can be overcome but also highlight potential limitations due to a high sensitivity with respect to numerical parameters. The methodology presented in this work introduces two novel ingredients compared with previous studies. Firstly, we use high-order discontinuous Galerkin methods for discretization in space. The inherent dissipation and dispersion properties of high-order discontinuous Galerkin discretizations are expected to render this approach well suited for transitional and turbulent flow simulations. Secondly, to mimic blinded CFD studies, we propose to use a precursor simulation approach in order to predict the inflow boundary condition for laminar, transitional, and turbulent flow regimes instead of prescribing analytical velocity profiles at the inflow. We investigate the whole range of Reynolds numbers as suggested by the FDA benchmark nozzle problem and compare the numerical results to experimental data obtained by particle image velocimetry in order to critically assess the predictive capabilities of the solver on the one hand and the suitability of the FDA nozzle problem as a benchmark in computational fluid dynamics on the other hand.

Geometrical effects in the hemodynamics of stenotic and non-stenotic left coronary arteries-numerical and in vitro approaches.

Abstract: Atherosclerosis is a common cardiovascular disease found in the left coronary artery (LCA), closely linked to local hemodynamic, which, in turn, is highly influenced by the artery geometry. The hemodynamics in the LCA was studied in a patient-specific geometry without any sign of disease using both numerical and in vitro approaches. The influence of non-planarity was evaluated through two models of the patient-specific LCA that deviate from its original geometry in their planarity. Afterwards, in all models, irregular stenoses were created by a procedure in which the stenosis emerges by diffusion from low wall shear stress (WSS) areas. The WSS distribution and flow patterns were evaluated in all the models. The experimental results validate the numerical code developed to study the blood flow assuming a steady state Newtonian behavior. Comparison between the planar and non-planar idealized LCA revealed no significant differences in low WSS regions forming stenotic regions with identical shape. In the patient-specific LCA, the low WSS regions are not consistent with the idealized models leading to a different stenosis shape. The results revealed that the non-planarity has an unquestionable effect in helicity. It was also demonstrated that eccentricity of the vessels cross section and the position of the apex in relation to the axis of the parent branch contribute to the flow patterns observed. Numerical results of pulsatile blood flow assuming a non-Newtonian behavior, in the patient-specific LCA, reinforce the non-planarity effect in local hemodynamics.

Coupled agent-based and hyperelastic modelling of the left ventricle post-myocardial infarction.

Abstract: Understanding the healing and remodelling processes induced by myocardial infarction (MI) of the heart is important, and the mechanical properties of the myocardium post-MI can be indicative for effective treatments aimed at avoiding eventual heart failure. MI remodelling is a multiscale feedback process between the mechanical loading and cellular adaptation. In this paper, we use an agent-based model to describe collagen remodelling by fibroblasts regulated by chemical and mechanical cues after acute MI, and upscale into a finite element 3D left ventricular model. We model the dispersed collagen fibre structure using the angular integration method and have incorporated a collagen fibre tension-compression switch in the left ventricle (LV) model. This enables us to study the scar healing (collagen deposition, degradation, and reorientation) of a rat heart post-MI. Our results, in terms of collagen accumulation and alignment, compare well with published experimental data. In addition, we show that different shapes of the MI region can affect the collagen remodelling, and in particular, the mechanical cue plays an important role in the healing process.