Computational Modeling and Simulation Examples in Bioengineering. Группа авторов

on the web should be of great help for lecturers when organizing classes. The theoretical presentations (either from the theoretical background or from bioengineering applications) can be accompanied by use of the software with menu‐driven modeling and solution display. Use of the software can also aid students when studying various theoretical or bioengineering problems.

The book will be also prepared to be useful for researchers in various fields related to bioengineering and other scientific fields, including medical applications. The book will provide basic information about how a bioengineering (or medical) problem can be modeled, which computational models can be used, and what is the background of the applied computer models.

      In Chapter 1, some basic theoretical and numerical examples of computational Modeling of Abdominal Aortic Aneurysms are given. Chapter 2 describes modeling the motion of rigid and deformable objects in fluid flow. Application of computational methods in dentistry with some theoretical and practical numerical examples is given in Chapter 3.

      Determining Young’s modulus of elasticity of cortical bone from CT scans is described in Chapter 4. Parametric modeling of blood flow and wall interaction in aortic dissection is investigated in Chapter 5. Application of AR Technology in Bioengineering is well described in Chapter 6. Augmented Reality Balance Physiotherapy is presented in Chapter 7. Modeling of the human heart – ventricular activation sequence and ECG measurement are given in Chapter 8. In Chapter 9, medical image segmentation was described using contrast stretching, edge‐detection and thresholds with coupled Simulink‐XSG (Xilinx System Generator) tool and FPGA (Field Programmable Gate Arrays). The book is intended for pre‐graduate and postgraduate students as well as for researchers in the domains of bioengineering, biomechanics, biomedical engineering, and medicine.

      Prof. Nenad D. Filipovic

      Faculty of Engineering,

      University of Kragujevac,

      SERBIA

1 Computational Modeling of Abdominal Aortic Aneurysms

       Nenad D. Filipovic1,2

       1Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

       2Bioengineering Research and Development Center, BioIRC, Kragujevac, Serbia

1.1 Background

      Abdominal aortic aneurysm (AAA) is a dilation of the aorta beyond 50% of the normal vessel diameter [1] that is frequently observed in the aging population [2] and it affects 6–9% of the people in the industrialized world. It is a major health problem that typically affects men after the age of 50 [3] and it is the thirteenth leading cause of death in Western societies [4]. AAAs cause about 15 000 deaths per year in the United States only [5], and 1.3% of all deaths among men aged 65–85 years in developed countries [6–8]. In the United States alone, 1.5 million have undiagnosed AAAs [3]. They can remain asymptomatic for most of their development and, if left untreated, they can enlarge and eventually rupture with catastrophic mortality rate of 80% [9] to 90% [2]. On the other hand, the mortality following elective AAA repair has significantly improved to 3–6% [10] which clearly demonstrates the need of diagnosing and monitoring AAAs on time in order to make progress in both medical and economic domain.

A myriad of different factors are established in the literature to account for AAA formation, expansion, and, eventually, rupture. Namely, a substantial amount of research on AAA expansion and rupture focuses on different biological and biomechanical factors and, lately, special attention is put to genes and chemical influences. Among biological factors and risk factors, authors usually discuss the influence of diameter, sex, blood pressure, chronic obstructive pulmonary disease, and smoking [11]. From a biomechanical point of view, major factors contributing to AAA expansion and rupture are the wall stress [12, 13] and strength [14], wall stiffness [15], vessel asymmetry [16–19], intraluminal thrombus (ILT) [20, 21], entire geometry [22, 23], etc. Namely, according to the biomechanical perspective, AAA rupture is usually defined as a material failure of the degenerated AAA wall to withstand the stress exerted on it [24]. However, the relationship between rupture and biomechanics of aorta proved to be more complex [10] and the evaluation based only on one of these parameters is not sufficient.

      For many years, diameter was taken to be the primary parameter associated with rupture risk estimation. Namely, the threshold of ≥5.5 cm³ was applied as indicative for AAA repair [10]. However, continuous studies in the past showed that while 10–24% of small aneurysms (<5.5 cm) may rupture [1], aneurisms which diameter exceeds the threshold remain stable. These findings cast doubt over the suitability of surgical repair based solely on the maximum diameter criterion [16–18]. In order to refute “diameter criterion” rule, many other criteria and parameters ensued. In contrast, the survey conducted in 2006 [25] confirmed that 92% of surgeons still use maximum diameter criterion and growth rate in making decisions about the surgical intervention while 19% of them stated that they were not even aware that biomechanics may influence the rupture risk. The results of the survey suggest that cooperation of surgeons and engineers is necessary in order not just to make technical advances but to implement them in practice.

      Mutual collaboration of clinicians and engineers resulted in different efforts and methodologies proposed in the last few decades, all striving to make progress in the domain of AAA expansion and rupture prediction. This paper reviews some of the most significant studies in the area of AAA modeling in the past decades which further understanding of utmost importance for making additional progress toward validation and application in the clinical setting. We firstly described computational methods applied for AAA in chronological order. Then, different experimental testing as well as our own testing is described in order to determine the mechanical properties of AAA. Mechanical–chemical including ILT modeling is separately analyzed. Finite element procedure including fluid–structure interaction (FSI) from our group is described. In the end, some of data mining (DM) approach and vision for future clinical decision support system (DSS) is given.

1.2 Clinical Trials for AAA

The known risk factors for AAA include male sex, smoking, hypertension, and a family history of AAA in a first‐degree relative [26]. Many clinical studies confirmed that smoking is the most important modifiable risk factor for AAA [27–33]. Some authors discovered that the duration of smoking and daily cigarette number are also associated with a higher risk of AAA [28, 34]. One large systematic review of studies evaluating smoking and aortic aneurysm placed the relative risk of aortic aneurysm‐related events in current smokers between 3 and 6 [34]. Smoking contributes significantly to the prevalence of AAA and may account for 75% of all AAA, 4 cm in diameter or larger [33].

      A history of hypertension and myocardial infarction or coronary artery bypass surgery was negatively associated, whereas a body mass index ≥25 kg/m² was protective [35].

1.3 Computational Methods Applied for AAA

      The study of Darling et al. [36] was one of the first studies to conclude that AAA rupture predominately occurs on posterior walls. Namely, in the autopsy study conducted almost 40 years ago on AAA patients, among whom 473 died with surgically intact arteriosclerotic AAAs, it was reported that 82% of ruptures occurred along the posterior and posteriolateral wall of AAA. Consequently, it was important to examine the wall stress acting