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Register a new userDevelopment of a patient-specific finite element model of the Transcatheter Aortic Valve Implantation (TAVI) procedure
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This work reports on a study to develop a patient-specific finite element model of the Transcatheter Aortic Valve Implantation procedure, using a model of a balloon-expandable percutaneous prosthetic aortic valve as a framework for the prediction of its performance. An experimentally measured left ventricle and aortic pressure profile of a single systolic-diastolic cycle of a resting heart are used to assess the performance of the stent after its deployment. The results of the simulation show that the stent maintains its structural integrity after deployment, and successfully pushes the native leaflets back to keep the aortic root clear of all impediments.
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This paper presents a new method for modeling the mechanics of the aortic valve, and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures, and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws, and gross morphology. These experiments suggest the following conclusions, which are directly applicable to the design of prosthetic aortic valves. (i) The loaded geometry, tensions and tangent moduli primarily determine model function. (ii) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (iii) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (iv) Valve performance is highly sensitive to free edge length and leaflet height. For future use, our aortic valve modeling framework offers flexibility in patient-specific models of cardiac flow.
An arteriovenous fistula, created by artificially connecting segments of a patients vasculature, is the preferred way to gain access to the bloodstream for kidney dialysis. The increasing power and availability of supercomputing infrastructure means that it is becoming more realistic to use simulations to help identify the best type and location of a fistula for a specific patient. We describe a 3D fistula model that uses the lattice Boltzmann method to simultaneously resolve blood flow in patient-specific arteries and veins. The simulations conducted here, comprising vasculatures of the whole forearm, demonstrate qualified validation against clinical data. Ongoing research to further encompass complex biophysics on realistic time scales will permit the use of human-scale physiological models for basic and clinical medicine.
A patient-specific fluid-structure interaction (FSI) model of a phase-contrast magnetic resonance angiography (PC-MRA) imaged arteriovenous fistula is presented. The numerical model is developed and simulated using a commercial multiphysics simulation package where a semi-implicit FSI coupling scheme combines a finite volume method blood flow model and a finite element method vessel wall model. A pulsatile mass-flow boundary condition is prescribed at the artery inlet of the model, and a three-element Windkessel model at the artery and vein outlets. The FSI model is freely available for analysis and extension. This work shows the effectiveness of combining a number of stabilisation techniques to simultaneously overcome the added-mass effect and optimise the efficiency of the overall model. The PC-MRA data, fluid model, and FSI model results show almost identical flow features in the fistula; this applies in particular to a flow recirculation region in the vein that could potentially lead to fistula failure.
In its permanent quest of mechanobiological homeostasis, our vascula-ture significantly adapts across multiple length and time scales in various physiological and pathological conditions. Computational modeling of vascular growth and remodeling (G&R) has significantly improved our insights of the mechanobio-logical processes of diseases such as hypertension or aneurysms. However, patient-specific computational modeling of ascending thoracic aortic aneurysm (ATAA) evolution, based on finite-element models (FEM), remains a challenging scientific problem with rare contributions, despite the major significance of this topic of research. Challenges are related to complex boundary conditions and geometries combined with layer-specific G&R responses. To address these challenges, in the current paper, we employed the constrained mixture model (CMM) to model the arterial wall as a mixture of different constituents such as elastin, collagen fiber families and smooth muscle cells (SMCs). Implemented in Abaqus as a UMAT, this first patient-specific CMM-based FEM of G&R in human ATAA was first validated for canonical problems such as single-layer thick-wall cylindrical and bi-layer thick-wall toric arterial geometries. Then it was used to predict ATAA evolution for a patient-specific aortic geometry, showing that the typical shape of an ATAA can be simply produced by elastin proteolysis localized in regions of deranged hemodymanics. The results indicate a transfer of stress to the adventitia by elastin loss and continuous adaptation of the stress distribution due to change of ATAA shape. Moreover, stress redistribution leads to collagen deposition where the maximum elastin mass is lost, which in turn leads to stiffening of the arterial wall. As future work, the predictions of this G&R framework will be validated on datasets of patient-specific ATAA geometries followed up over a significant number of years.
Invasive intracranial electroencephalography (iEEG) or electrocorticography (ECoG) measures electrical potential directly on the surface of the brain, and, combined with numerical modeling, can be used to inform treatment planning for epilepsy surgery. Accurate solution of the iEEG or ECoG forward problem, which is a crucial prerequisite for solving the inverse problem in epilepsy seizure onset localization, requires accurate representation of the patients brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative MRI is incompatible with implanted electrodes and CT has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electrical potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the leadfield matrix also showed significant differences between the different models. The results suggest that significant improvements in source localization accuracy may be realized by the application of the proposed modeling methodology.
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