Computational Modeling and Simulation Examples in Bioengineering. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ volume, surface area, bulge height, and ILT volume were all highly correlated with rupture status. In this study, the overall classification accuracy was 86.6%. It used a decision tree algorithm that is one of the possible machine learning methods that can be used for large data sets requiring a decision output.

      Filipovic et al. [121] combined DM techniques and CFD for the estimation of the wall shear stresses in AAA under prescribed geometry changes. They performed large‐scale CFD runs for creating machine learning data on the Grid infrastructure and their results showed that DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data.

      The abovementioned studies have been limited by the use of geometric parameters and, in particular, the maximum diameter of lumen alone as factors contributing to the rupture of an AAA. But other parameters such as patient history and comorbidities and presence of stents or other geometric parameters such as the aneurysm neck angles, tortuosity, and genetics factors [122–126] should be included. Despite the fact that state‐of‐the‐art FEM approaches represent powerful tool for estimation of AAA, their application in clinical practice remains limited due to several reasons. Firstly, every patient has specific and complex anatomy and it is not possible to create a general or parametric human model. Moreover, accuracy of simulations depends on the considered level of details, meaning that increasing required computation time and power will be necessary for obtaining precise results. As a consequence, performing patient‐specific simulation may take a few hours (if patient scans are available in the first place). This makes current FEM approaches inadequate for urgent situations such as alerting patient in case of AAA rupture.

      In order to avoid described limitations, the patient‐specific DSS could be proposed. The main idea is to perform patient‐specific forward simulations in advance. AAA of different types (sizes and positions) will be simulated and calculated stress analysis will be used for training of intelligent model. But, in order to perform forward FEM simulations patient geometry is required. For this reason, during the registration into our system, in local workstation (user hospital), patient or his/her medical institution will be asked to provide us patients' medical scans (CT or MRI for example), if there are any. However, it is assumed that some patients will not have medical scans.

For future clinical DSS, we suggest the following scenario: new user has already performed medical scanning (CT or MRI) and these data are uploaded to the central server (or they are available over institution existing connection). In that case, we will generate 3D patient‐specific FEM model by using image‐based modeling software developed on the server. It is important to mention that, for the project purposes, accurate reconstructions will be necessary only for torso and aorta. We will reconstruct only aorta (by using statistical shape models) and torso interface (by using level set and marching cubes algorithms) because they are needed for setting FEM boundary conditions and because, on the other hand, accurate reconstruction of all organs represents time‐consuming and computationally expensive problem with current medical imaging methods. In this way, highly accurate geometry, materials, and boundary conditions could be obtained for every patient (Figure 1.4).

Figure 1.4 Description of clinical decision support system for AAA disease.

1.9 Conclusions

      In the first part of this paper, literature survey on AAA biomechanics is reported including several aspects from experimental test, constitutive model, and ILT. Further, we presented our FE models, aimed at simulating and enhancing the computational study of the aneurismatic pathology. The combination of fluid and nonlinear structure modeling can give better understanding of the flow, pressure distribution, wall shear stress quantification, and effect of material properties and geometrical parameters. Computational methods have made patient‐specific analyses possible, a feature essential for understanding the progression of AAA in a particular patient. Finally, future clinical DSS is suggested by using DM approach. The main aim is to run predictive FSI model in order to estimate the risk of rupture and to use patient‐specific wall properties with calcium, tissue disease, and thrombus to overcome multiple level of uncertainties.

      During the last two decades, significant efforts have been made in order to define a computational model which includes biomechanical and biological approach, but still a lot of clinical studies are necessary in order to make these computational studies real in everyday clinical practice.

      Exercise 1.1 Modeling of Blood Flow Within the AAA

      The shape of AAA is very important. The severity of AAA is commonly estimated in clinical practice by considering the AAA maximal diameter. However, from the mechanical point of view, the hemodynamic effects and the mechanical stresses within the AAA tissue certainly are important in the process of the AAA rupture. Bulge diameter alone may not be a sufficient criterion for determination of rupture risk; therefore, an insight into the hemodynamic effects and the stress–strain quantification and distribution within the vessel wall are of great significance even in medical practice.

      Generation of the Finite Element Model

A simplified geometry of an aneurism is shown in Figure 1.5. With the on‐web software, the 3D finite element model for the blood flow domain can be parametrically generated. A transition smoothness between the surfaces is achieved by using Bezier's curves. Also, the results can be displayed with a user‐friendly menu in a way suitable for an insight into medical aspects of the blood flow conditions. A detailed description of the software use is given on the web (within Tutorial of each example), while the description of geometric parameters is given in the caption of Figure 1.5.

Figure 1.5 Geometrical parameters of AAA: “Length” is the parameter which defines the total horizontal projection of the generated aneurysm model; “A” is the height of the arc of central line; “Aorta diameter” is the abdominal aorta diameter; “B” is the radius from the central line to the inner wall of the aneurysm; “C” is the radius from the central line to the outer wall of the aneurysm; “Aneurysm length” is an average length of the AAA.

      Boundary Conditions

      At the inflow aorta cross‐section, a fully developed parabolic flow is assumed, determined by a selected volume flux. The normal stress and tangential stress are set to be equal to zero (stress‐free condition) or they are prescribed at the outlet cross‐section.

It is assumed that the entering flow is pulsatile, with a typical waveform shown in Figure 1.6 [127]. As described in the software menu, the waveform can be changed.

Figure 1.6 A typical in‐flow waveform at the aorta entry. Q is the volumetric in‐flux and t/T is the СКАЧАТЬ