Acquisition of 3-D Arterial Geometries and Integration with Computational Fluid Dynamics (original) (raw)
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Medical Physics, 2008
A 3D model reproducing the biomechanical behavior of human blood vessels is presented. The model, based on a multilayer geometry composed of right generalized cylinders, enables the representation of different vessel morphologies, including bifurcations, either healthy or affected by stenoses. Using a finite element approach, blood flow is simulated by considering a dynamic displacement of the scatterers Í‘erythrocytesÍ’, while arterial pulsation due to the hydraulic pressure is taken into account through a fluid-structure interaction based on a wall model. Each region is acoustically characterized using FIELD II software, which produces the radio frequency echo signals corresponding to echographic scans. Three acoustic physiological phantoms of carotid arteries surrounded by elastic tissue are presented to illustrate the model's capability. The first corresponds to a healthy blood vessel, the second includes a 50% stenosis, and the third represents a carotid bifurcation. Examples of M mode, B mode and color Doppler images derived from these phantoms are shown. Two examples of M-mode image segmentation and the identification of the atherosclerotic plaque boundaries on Doppler color images are reported. The model could be used as a tool for the preliminary evaluation of ultrasound signal processing and visualization techniques.
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IEEE Transactions on Medical Imaging, 2004
Three-dimensional (3-D) ultrasound is a relatively new technique, which is well suited to imaging superficial blood vessels, and potentially provides a useful, noninvasive method for generating anatomically realistic 3-D models of the peripheral vasculature. Such models are essential for accurate simulation of blood flow using computational fluid dynamics (CFD), but may also be used to quantify atherosclerotic plaque more comprehensively than routine clinical methods. In this paper, we present a spline-based method for reconstructing the normal and diseased carotid artery bifurcation from images acquired using a freehand 3-D ultrasound system. The vessel wall (intima-media interface) and lumen surfaces are represented by a geometric model defined using smoothing splines. Using this coupled wall-lumen model, we demonstrate how plaque may be analyzed automatically to provide a comprehensive set of quantitative measures of size and shape, including established clinical measures, such as degree of (diameter) stenosis. The geometric accuracy of 3-D ultrasound reconstruction is assessed using pulsatile phantoms of the carotid bifurcation, and we conclude by demonstrating the in vivo application of the algorithms outlined to 3-D ultrasound scans from a series of patient carotid arteries.
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Obtaining detailed, patient-specific blood flow information would be very useful in detecting and monitoring cardio-vascular diseases. Current approaches rely on computational fluid dynamics to achieve this; however, these are hardly usable in the daily clinical routine due to the required technical supervision and long computing times. We propose a fast measurement enhancement method that requires neither supervision nor long computation and it is the objective of this paper to evaluate its performance as compared to the state-of-the-art. To this purpose a large set of abdominal aortic bifurcation geometries was used to test this technique and the results were compared to measurements and numerical simulations. We find that this method is able to dramatically improve the quality of the measurement information, in particular the flow-derived quantities such as wall shear stress. Additionally, good estimation of unmeasurable quantities such as pressure can be provided. We demonstrate that this approach is a practical and clinically feasible alternative to fully-blown, time-consuming, patient-specific flow simulations.
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Numerical simulation of fluid-structure interaction (FSI) in the arterial system is a challenging and time consuming procedure because of the intrinsic heterogeneous nature of the problem. Moreover, in patientspecific simulations, modeling of the vascular structure requires parameter identification still difficult to accomplish. On the other hand, new imaging devices provide time sequences of the moving vessel of interest. When one is interested only in the blood dynamics in the compliant vessel, a possible alternative to the full fluid-structure interaction simulation is to track the vessel displacement from the images and then to solve the fluid problem in the moving domain reconstructed accordingly. In this paper, we present an example of this image-based technique. We describe the steps necessary for this approach (image acquisition and 3D geometric reconstruction, motion tracking, computational fluid dynamics (CFD) simulation) and present some results referring to an aortic arch and a validation of the proposed technique vs. a traditional FSI simulation in a carotid bifurcation. This approach significantly reduces the CPU time since the dynamics of the structure is retrieved from the images instead of being numerically computed. This work places itself in the framework of a strong integration between data (images/measures) and simulations that is likely to introduce a significant improvement in the reliability of cardiovascular numerical mathematics. . Prepared using cnmauth.cls [Version: 2010/03/27 v2.00] 2 M. PICCINELLI ET AL for healthy subjects. The nonlinearities of the model result however in computational difficulties.
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One of the techniques used to diagnose carotid artery disease is the ultrasound (US) examination. The initiation and development of vascular diseases depends also on the flow conditions in the artery. Additional parameters that cannot be directly measured can be obtained by performing numerical simulations using patient-specific geometry. In this study, the Finite Element Method (FEM) is used to analyze the distribution of relevant blood flow characteristics. Images obtained from the US examinations are used to adapt the generalized carotid bifurcation model to the specific patient. The approach presented in this study combines the deep learning approach for the image segmentation and automated 3D reconstruction method to create a semi-generic geometrical model of the carotid artery that is adapted to the specific patient, using data obtained from only several US images. The presented methodology enables efficient segmentation, extraction of the morphological parameters and creation...
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Abdominal aortic aneurysm (AAA) is a life-threatening disease when the diameter exceeds its safety margin or the aortic wall reaches its mechanical strength. Both of these potential problems need to be clinically assessed. The geometrical influence on blood flow behavior and hemodynamic changes of AAA are not fully clinically understood. The study aimed to explore creating comprehensive and detailed models for use in reconstruction, modeling, and simulating three-dimensional (3D) patient-specific geometry based on two-dimensional (2D) computed tomography images. The patient information was extracted from computed tomography images and the AAA patient's database. The 3D geometrical models were created using MIMICS software segmentation tools and exported in STL files to ANSYS Workbench. Computational fluid dynamics (CFD) and finite volume methods were used to solve Navier-Stokes equations for fluid flow in the 3D domain. Blood was treated as incompressible and Newtonian fluid, and a transient flow with a time-dependent velocity waveform assigned at the inlet boundary. The computational results were visualized using ANSYS Fluent post-processing. The CFD transient simulation results are presented using the hemodynamic parameters, including velocity vectors, flow patterns (streamlines), pressure distribution, and wall shear stress. The demonstrated results are part of the study aims and methods in order to provide detailed approaches of computational analysis. The procedures used in this study would be useful for understanding the biomechanical influence on blood flow and hemodynamics.