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Computers in Biology and Medicine 43 (2013) 377–385 Contents lists available at SciVerse ScienceDirect Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/cbm Review on CFD simulation in heart with dilated cardiomyopathy and myocardial infarction Bee Ting Chan a,n, Einly Lim a,1, Kok Han Chee b,2, Noor Azuan Abu Osman a,3 a b Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia Department of Medicine, Faculty of Medicine Building, University of Malaya, 50603 Kuala Lumpur, Malaysia a r t i c l e i n f o abstract Article history: Received 12 April 2012 Accepted 20 January 2013 The heart is a sophisticated functional organ that plays a crucial role in the blood circulatory system. Hemodynamics within the heart chamber can be indicative of exert cardiac health. Due to the limitations of current cardiac imaging modalities, computational fluid dynamics (CFD) have been widely used for the purposes of cardiac function assessment and heart disease diagnosis, as they provide detailed insights into the cardiac flow field. An understanding of ventricular hemodynamics and pathological severities can be gained through studies that employ the CFD method. In this research the hemodynamics of two common myocardial diseases, dilated cardiomyopathy (DCM) and myocardial infarction (MI) were investigated, during both the filling phase and the whole cardiac cycle, through a prescribed geometry and fluid structure interaction (FSI) approach. The results of the research indicated that early stage disease identification and the improvement of cardiac assisting devices and therapeutic procedures can be facilitated through the use of the CFD method. & 2013 Elsevier Ltd. All rights reserved. Keywords: Computational fluid dynamics Dilated cardiomyopathy Myocardial infarction Contents 1. 2. 3. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Clinical measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 CFD approaches to simulate Blood flow in DCM and MI conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 3.1. Dilated cardiomyopathy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 3.2. Myocardial infarction (MI). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 4. Future direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Acknowledgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 1. Introduction Heart failure is characterized by the inability of the heart to supply adequate blood flow and therefore deliver oxygen to tissues and organs in the body. It is usually induced by cardiovascular diseases n Corresponding author. Tel.: þ60 1 6752 3764; fax: þ60 3 7967 7661. E-mail addresses: [email protected] (B.T. Chan), [email protected] (E. Lim), [email protected] (K.H. Chee), [email protected] (N.A. Abu Osman). 1 Tel.: þ60 1 2212 3632; fax: þ60 3 7967 7661. 2 Tel.: þ60 1 7695 2750; fax: þ 60 3 7953 5627. 3 Tel./fax:þ 60 3 7967 7661. 0010-4825/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compbiomed.2013.01.013 such as coronary artery disease, arrhythmias, congenital heart disease, heart valve disease, congestive heart disease, rheumatic heart disease, stroke and high blood pressure. Cardiovascular disease is the most commonly reported cause of mortality and it contributes to approximately 30% of deaths worldwide [1]. When an individual suffers from cardiovascular disease a number of compensatory mechanisms take place in order to maintain cardiac output and these include geometrical modifications of the heart. The heart is stretched to hold more blood in the diastole so that it is able to generate stronger force of contraction during systole, following Frank–Starling’s law [2–4]. Furthermore, to overcome the high afterload pressure that is normally experienced by patients that are suffering from heart disease, yet with reasonable wall stress, the muscle wall thickens. Over a long 378 B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 period the gradual declination of cardiac performance is no longer compensable. At this stage, clinical symptoms such as fatigue, dizziness, diminished exercise capacity, shortness of breath and edema are observed. Heart failure patients may experience systolic dysfunction, diastolic dysfunction, or both. Systolic dysfunction refers to the abnormal performance of the heart caused by insufficient contraction, while diastolic dysfunction refers to abnormalities that are caused by the insufficient relaxation of the heart. Systolic dysfunction involves a progressive condition which leads to cardiac remodeling, which is characterized by dilatation, changes in sphericity, wall thinning, decreased cardiac reserve, impaired exercise tolerance, increased wall stress [5] and thus increased myocardial oxygen demand [6]. DCM is one of the most common cardiac diseases which exhibits systolic dysfunction. Diastolic dysfunction, on the other hand, is characterized by slow relaxation of the heart and abnormal filling patterns, most often caused by increased stiffness of the cardiac muscle [7–8]. Diastolic heart failure is shown in hypertrophy cardiomyopathy where the heart develops thicker and stiffer heart muscles that show signs of impaired relaxation. Numerous methods have been used to diagnose and differentiate various types of heart failure conditions in order to devise the best treatment strategies for the patients involved. These involve examining the heart’s morphology [9–11], electrical activity [12–13], mechanics [11,14–15] and hemodynamics [16–17]. Invasive diagnostic methods, such as blood test and coronary catheterization procedures, are routinely used in a clinical setting. With the advancement of medical technologies, noninvasive imaging modalities, such as chest X-rays, electrocardiograms (ECG), computed tomography (CT) and magnetic resonance imaging (MRI) are gradually becoming more popular. Among these, MRI and echocardiography are the most commonly used diagnostic tools that are used to assess cardiac function through geometric and flow measurements, such as left ventricle (LV) volume, wall mass, stroke volume, ejection fraction (EF), wall motion and wall thickness. However, differentiated velocity vector and pressure field, as well as the local hemodynamics indices such as mass transport, wall shear and boundary flow layer [18], which are important parameters for early diagnosis of heart failure, are not able to quantitatively evaluate the effects of individual parameters to the disease conditions through imaging modalities. The CFD method involves the study of cardiovascular blood flow patterns and it has emerged as a reliable tool that can be used to enhance our understanding of the pathophysiology and progression of heart disease by providing the means through which reproducible numerical experiments can be produced under identical conditions. Global and regional hemodynamics variables, such as intraventricular blood flow dynamics, ventricular wall motion, spatial and temporal distributions of pressure and myocardial strains and stresses, can be obtained through the simulations. Information about these parameters provides opportunities for the early diagnosis of certain heart diseases while sensitivity analysis performed through the CFD method is able to demonstrate the correlation between individual parameter to the disease condition. Early CFD techniques for hemodynamics simulations were mostly carried out on simplified geometries [19–21]. With the development of cardiac imaging techniques, patientspecific morphology and flow have been progressively used [22–25], and this provides valuable clinical information. In the recent decade, FSI models that take into account the interaction between the blood and the cardiac wall have been developed [26–30]. The present paper provides a comprehensive overview of the existing diagnostic methods, including CFD simulations, in terms of their ability to identify the presence of the two most common myocardial diseases, i.e. DCM and MI during filling phase. Currently available and commonly used diagnostic tools are presented, with an emphasis on the various information or parameters they provide, as well as their limitations on global variables. This is followed by a review of the existing CFD models of the diseases, focusing on their methods, findings (the global and regional hemodynamic variables), as well as validations of the results. 2. Clinical measurements As a result of heart failure a series of examinations are carried out for the purpose of inspecting cardiac function. Cardiac morphology is noninvasively measured through CT, MRI and echocardiography. The parameters, such as wall thickness, end diastolic dimension (EDD) and end systolic dimension (ESD), are acquired in linear or volumetric measurements. In cardiac mechanics assessment, the LVEF is the gold standard for cardiac systolic function [31]. This global index represents ventricular contraction strength, which is easily obtained through MRI and 2D echocardiography. Myocardial contractility is usually inspected through wall motion, which is detected in tissue Doppler imaging. The diastolic function is currently indicated by deceleration time and E/A ratio [32], which are normally attained during the LV filling phase through pulsed wave Doppler echocardiography. To some extent, M-mode Doppler and MRI flux measurements provide hemodynamics information such as flow velocity propagation. The hallmark of DCM is ventricular dilatation with myocardial contractile dysfunction [33], where reducing wall thickness and ventricular chamber enlarge around 4 cm yields spherical ventricular shape. Commonly under measurement of MRI [34] and echocardiography [35–37], it shows increased EDV (greater than 112%) and ESV with thinner wall in anatomical data, while functionally with low EF (less than 45%) and decreased wall motion with fractional shortening less than 25%. Mitral regurgitation flow is considered to be a hemodynamic disorder [37]. MI is indicated by a bulge found at the affected area, thus resulting in an irregular ventricular shape under MRI [38–40] and echocardiograph [41–42]. The bulge area has a wall thickness 30% thinner than the adjacent segment [43] and this is usually less than 5.5–6 mm. Due to regional contractility loss, MI shows slightly lower EF compared to global effect on DCM. However, MI may develop into DCM due to the over stretching of the muscle at normal region to compensate infarcted region mechanism [44]. Meanwhile hemostasis region is observed as an identification of abnormal blood flow pattern [45–46]. Although global LV function depends on both systolic and diastolic functions, the findings from imaging modalities conclude systolic dysfunction in DCM and MI through EF reduction [47–48], while the gauge of diastolic dysfunction is not yet well-found. Although E/A ratio has been widely used to assess diastolic dysfunction, recent studies [49–51] question its efficiency in certain disease conditions. The pseudonormal condition shows normal E/A ratio due to the effects of impaired relaxation and restriction, which compensate each other. It is therefore highly likely that this can lead to an incorrect diagnosis. Hereby hemodynamics and combination imaging techniques [36,52] are required to reveal the abnormalities. Yet, the evaluation of cardiac function has traditionally been limited to global geometric measurements which restrict the information based on the regional assessments due to temporal and spatial resolution constraint. Regional measurement and hemodynamics play crucial roles in disease initiation and B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 progression. In advanced MR function, measurements pertaining to the regional myocardial functions are available through MR tagging [53], however it is a time consuming process. 3. CFD approaches to simulate Blood flow in DCM and MI conditions The CFD approach provides approximate solution of velocity and pressure fields through Navier–Stokes equation [54]. In terms of its strength over current imaging diagnostic tools, CFD further provides hemodynamics factors such as intraventricular flow dynamics, wall shear, mass transport and stagnation region [55]. The velocity and vorticity distribution have a complicated relationship in thrombus formation [56], which is important during the identification of cardiac disease. Hereby various physical flow conditions can be predicted, while inducing the early recognition of disease stage helps to reduce patient’s risk. As a result, CFD simulation has been extensively applied as a means of investigating ventricle pathological flow [57–58] and evaluating surgical treatment and cardiac assist devices. Existing studies that examine CFD as a means of examining the common myocardial diseases of DCM and MI are discussed in this review study. 3.1. Dilated cardiomyopathy The blood flow distribution within dilated LV was studied during the filling phase [59] and the whole cardiac cycle [60]. LV was represented in a simple axisymmetrical geometry (spheroid). Hemodynamics at different stages of DCM were analyzed and compared with that of a healthy LV. During the filling phase study [59], LV was characterized with dimension D representing diameter and H 379 representing height of the left ventricle. The different stages of DCM were modeled according to end diastole linear relation D ¼ 0:04a þ 0:05 ½m; H ¼ 0:02aþ 0:09 ½m formulated through clinical data. The value of a varies from 0 (healthy) to 1 (severe dilatation). The author simulated a healthy heart as well as three different stages of DCM condition to study the relationship between LV dilatation and flow distribution. The blood flows into the left ventricle through the mitral orifice interact with the left ventricular wall. The pulsatile flow rate obtained from clinical data is applied as the inlet boundary condition. The wall motion is prescribed with change of diameter and height of LV according to the pulsatile flow rate. In healthy LV, two vortex rings are formed during the filling phase, where the primary vortex ring grows in size and propagates toward the apex during early of diastole and a smaller secondary vortex ring is then formed at basal region at late of diastole. High propulsion energy is obtained through great velocity and vorticity strength. In dilated LV, only one vortex ring is observed and it remains attached to the valvular edge throughout the filling phase (Fig. 1). The attachment is induced by wake formation, which slows down vortex impulse and propagation toward the apex. When the volume of LV dilatation increases, the size of the vortex ring becomes larger with lower propagation velocity. The stagnant flow is observed in the lower apical region, which prompts the condition of thrombosis. The existence of DCM leads to continuous deterioration of the cardiac function. The left ventricular assisted device (LVAD) was invented as a means of improving poor cardiac function by regulating flow within the LV. The role that LVAD plays in supporting DCM heart was investigated throughout the cardiac cycle [60]. A simplified ellipsoid geometry was connected to cylindrical tubes at Fig. 1. Flow distribution in healthy LV (left) with A¼ 0 at t ¼3/64 (top), t¼ 6/64 (middle) and t ¼21/64 (bottom); dilated LV(right) with A ¼ 0.25, t¼ 15/64 (top), A ¼ 0.5, t ¼20/64 (middle) and A ¼ 0.7, t ¼22/64 (bottom) [59]. 380 B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 Fig. 2. PV loops for healthy and DCM ventricles [60]. both the top (inlet) and bottom (outlet). A lumped parameter model consists of systemic and pulmonary circulation that is made of ventricular chamber, valves, venous and arteries in electrical circuit. A LVAD was modeled by connecting a flow pump between the LV and the aorta. The volume change in the LV was derived from a circulatory model and used to obtain fluid or wall velocity. Aside from the healthy LV, end stage DCM and assisted DCM with various LVAD flow rates were simulated for the purpose of comparison. The end stage of DCM has large ventricular volume and lower ejection fraction compared to healthy LV. With increasing flow rate in LVAD, it was found that ventricular volume decreases and ejection fraction increases, which shifts the pressure–volume (PV) loop to the left (Fig. 2). Instead of two vortex rings that were observed in the first study [59], another third vortex ring was then observed in the Fig. 3. Flow fields in healthy LV (top), DCM (LVAD flow¼ 0 L/min) (middle) and DCM (LVAD flow¼ 6 L/min) (bottom): (a) maximum filling rate; (b) end of filling phase; (c) maximum ejection rate; and (d) end of ejection phase [60]. B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 healthy LV during the end of filling. This vortex rings developed and rotated in opposite directions as a result of the interaction between the first and second vortex rings and the wall of the LV. The vortex ring then disappeared during the ejection period (Fig. 3: top). However, with end-stage DCM, the LV exhibited lower mitral flow and smaller vortex rings were observed at the beginning of filling. The vortex ring stayed near the apex during ejection and a small part of the next filling phase (Fig. 3: middle). The vortex ring size deviated from the first study because the models were built with different DCM severity, where the end-stage DCM model built was too weak and this entailed that the incoming jet was no longer able to induce a strong vortex, hence dilatation would not produce the larger vortex. With the assistance of the LVAD in the DCM heart, the flow field was corrected to replicate that of a normal heart (Fig. 3: bottom). The increase in the LVAD flow rate decreased the volume of the LV and, as such, vortex formation was more effective. The vortex strength, area and vorticity were also improved. Fig. 4. Distribution of normal, infarcted and border zone region [66]. 381 3.2. Myocardial infarction (MI) FSI analysis was used to investigate the influence of MI on LV hemodynamics. A LV was built that had a composition of 40% infarction region at apex, 10% at the border zone region and 50% at normal region at the base (Fig. 4). The properties of infarcted tissue change with time, where the stiffness initially reduces and then progressively increases after days or weeks of infarction. The contractility of infarcted and border zone region is 30% and 70% of the normal region respectively. The same preload and afterload were applied for all the conditions as the objective of the study was to investigate differences in mechanical behavior between a normal and a diseased heart, excluding compensatory mechanism. In the LV with MI, the myocardial wall at the normal region becomes thick while it is activated, the infracted region is inactivated and stretched to become thinner and bulged (ventricular aneurysm), as shown in Fig. 5. Flow velocities are lower in the ventricular aneurysm, 10 cm/s during systole and 5.16 cm/s during diastole compared to velocity of systole 15.6 cm/s and diastole 9.1 cm/s at the normal region. This suggests that there is a causal relationship between aneurysm formation and ventricular thrombus via blood stagnation. The flow abnormality, intraventricular flow and pressure gradient were analyzed throughout the cardiac cycle in normal and acute infarction left ventricle (Fig. 6). During systole (0.2 s), the whole normal LV filled with ejection flow directing to aortic valve and a monotonous pressure gradient was observed from the apex to the aortic outflow. However, the ejection flow in the infarcted left ventricle existed only in the upper half, even the flow directing toward apex was observed. Higher pressure in the upper portion of LV was detected because myocardial tissue in the normal region generates contraction force. During diastole (0.8 s), large vortex was observed in both the infarcted and healthy LV. However in the LV with MI, the vortex shape was distorted and presented a more rectangular shape where its center shifts toward the apex. Meanwhile, the energetic efficiency for the LV models was investigated. The energy efficiency for the healthy LV, subacute and acute MI was 82.1%, 71.2% and 68.3% respectively. It can be concluded that the LV with MI is less efficient than that of a healthy LV. MI is often closely related to wall motion abnormalities due to impaired contractility of injured muscle. The decreased wall Fig. 5. Intraventricular flow pattern and wall displacement [66]. 382 B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 Fig. 6. Intraventricular flow pattern and pressure distribution during systole (0.2 s) and diastole(0.8 s) in normal and infarcted LV [66]. motion can be classified as hypokinetic (reduced wall motion), akinetic (absent wall motion) and dyskinetic (bulge). Thus the relationship between wall motion abnormalities and intraventricular fluid dynamics is of more interest to researchers [61]. LV was modeled as a truncated prolate spheroid to provide basic insight into ventricular flow. The ventricular wall motion is formulated by reducing longitudinal and circumferential strain from healthy to infarcted LV. At instant of time, healthy strain at every point was multiplied by reduction coefficient to reduce contraction, " # yyc 4 ZZc 4 cðZ, yÞ ¼ 1A exp 2ys 2Zs where infarction region has center at yc ¼ 5p=6 and Zc ¼ p=8, and extension ys ¼ p=3 and Zs ¼ p=10; factor A represents infarction entity, where A¼0 is a healthy LV without infarction, while regional akinesia is represented by A¼1 and A¼ 2 denotes dyskinetic condition. The sets of results were analyzed in terms of vorticity and velocity. During diastole, vortex rings were observed with primary vortex deeply penetrating into the LV. During early diastole, compact vortex rings with two counter-rotating vortices were observed. The anterior vortex occupied a large part of the LV center and pushed the posterior vortex (Fig. 7: top (a)) creep along posterior wall that interacted with the viscous boundary layer, which partly dissipated within the boundary layer. At late of diastole, a secondary vortex was shaped in the basal region. During systole, vortices interacted and produced complex 3D field (Fig. 7: top (d)) which then dissoluted and ejected. In the LV with akinetic wall motion, the vortex penetrated less deeply into the LV and remained in the basal region during diastole (as shown in Fig. 7: middle (a)). It had a lower level of vorticity and strength. During systole, the flow had low velocity and a more regular vortex was exposed (Fig. 7: middle (c)) due to weaker self-induced dynamics and dissipation. The ejection fraction reduced from 55% acquired in the healthy LV to 45%. The dyskinetic wall motion had a lower level of vorticity with increased shortening of mitral jet (Fig. 7: bottom). The small vortex is formed in the upper basal region. During systole (Fig. 7: bottom (c)), flow redirected to aortic outflow from the central and upper regions of the LV, while null velocity was spotted close to the apex area. This yielded a much lower ejection fraction of 32%. This study ascertained that flow was stagnant at the apex and close to the wall segment with reduced mobility due to insufficient vortex strength to wash out the fluid. Hence blood clot formation or thrombosis tends to take place in the infarcted LV. Surgery is one option for the treatment of myocardial infarction. An investigation into the effect of surgical ventricle reconstruction (SVR) on patient-specific ventricle was carried out [62]. The cardiac MR images of the MI patient were transformed into grid images and applied in Karlsruhe Heart Model (KaHMo) [63] B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 383 Fig. 7. Flow in healthy LV (top), LV with moderate regional akinetic (middle), LV with regional dyskinetic (bottom): during diastole (a), (b); and systole (c), (d) [61]. Fig. 8. KaHMo flow simulation in healthy LV (volunteer) and DCM patient before surgery (pre-op) and after surgery (post-op) [62]. as prescribed movement for LV flow analysis. KaHMo circulation model provided pressure boundary conditions, while fluid velocity and pressure were solved with electric resistance, inductivity and compliance of arteries and venous by taking the rheological properties of blood into consideration. A healthy LV and infarcted LV before and after surgery were simulated in the study. The infarction occurred at the anterior of the apex. In the healthy LV, the vortex rings were observed but these were asymmetrically formed during the filling phase. The vortex rings expanded and rotated in a clockwise direction to redirect intraventricular blood, including blood at the apical region, toward the aortic outflow. The secondary vortex ring, which was newly formed in the basal region, was flushed out, together with the blood stream during systole (Fig. 8: volunteer). In the infarcted LV before surgery, aneurysm in apical region caused the deformation of the vortex where the blood branch in the middle of the LV resulted in flow loss during diastole. The flow during systole coincided with the flow pattern of the healthy LV (Fig. 8: pre-op). With the healthy LV, the same topologic LV structure was observed in the infarcted patient before surgery. However, the flow structure of the blood after surgery was completely different. Removing the aneurysm region from the infarcted LV produced a ball-shaped LV geometry and this had a significant effect on intraventricular flow (Fig. 8: post-op). During diastole, the inflow jet did not decelerate enough and a stagnant region was found at the apex. The change of geometry shape also restricted the ring vortex expansion area; as such, blood was not redirected toward the aortic outlet during systole. The improved EF but slightly lower stroke volume obtained from the infarcted LV after surgery (post-operation) compared to before surgery (pre-operation). Also the exchange transfusion parameter was evaluated corresponding to the remaining 384 B.T. Chan et al. / Computers in Biology and Medicine 43 (2013) 377–385 fractional blood volume in the LV after a number of cardiac cycles. It was found that blood washout in the LV of post-operation was slightly different from the pre-operation condition. Based on this result, the ball-shaped LV showed impaired fluid dynamics but was not deemed to be of greater risk of thrombosis formation. 4. Future direction CFD greatly contributes to cardiac diagnosis with condition that accuracy of simulation result is promised. Due to the limitation of certain aspects, assumptions are always made when the CFD method is used. As such, result validation is important, where well-matched outcome with imaging modalities and experiment helps in justification. In order to have exactly compatible results, the CFD boundary conditions should be as realistic as possible. The integration of other chambers i.e. LA and RV, as well as complex surface topography of the ventricle, such as trabeculae and papillary muscles, would strengthen the reliability of CFD study. For this reason, patient-specific CFD models have been developed with conjunction use of boundary conditions extracted from imaging technology. Cardiovascular CFD studies have progressively stepped forward to fluid structure interaction (FSI), which tends to provide more realistic cardiac flow analysis with myocardial mechanics interaction. Furthermore, the material properties of blood and myocardium play crucial roles in response to pragmatic disease states, for instance, non-Newtonian blood and myocardial viscoelasticity properties are contemporary representation. Ultimately, a whole cardiac model that integrates geometrical, mechanical, electrical and hemodynamics functions would be useful in total heart function representation [64]. After years of stability and concrete assurance, CFD should stand in place of clinical practice. CFD provides insights into cardiac hemodynamics and disease causation and this directly helps in the detection of diseases during the initial stages, which can increase survival rates. In addition, interventions of therapeutic and surgical procedures, as well as cardiac assisting devices, are optimized through CFD trends. 5. Conclusion To inspect flow abnormalities within a diseased heart, it is important to have knowledge of the blood flow process and distribution in a healthy heart [65], which is characterized by efficient and consistent flow patterns. Previous studies have shown that the effects of pathological LV on intraventricular flow depend on the severity of the disease. Different pathological patterns and affected areas have different effects on the blood flow pattern. The DCM and MI are characterized by enlarged LV chambers and reductions in wall motion, both globally and regionally. The blood flow pattern deviation was shown through vortex dynamics while systolic function was evaluated in EF. Thrombus and its potential formation were detected in DCM and MI through the use of the CFD method. A prescribed geometry [59–62] and the FSI [66] method in CFD approach were used to study hemodynamics within the LV chamber, where the outcomes are both presented. CFD demonstrates hemodynamics evolution and is able to predict the effects of any changes within the pathological LV. Therefore strong comprehension in the pathophysiology of heart disease is developed and this can be used to stimulate the invention of a heartassisting device, as well as identifying methods of improving surgical procedures. Conflict of interest statement I declare that we do not have any financial or personal relationship with other people or organizations that could have inappropriately influenced this study. Acknowledgment This study was funded by UM/MOHE HIR, Grant number UM.C/ HIR/MOHE/ENG/14 D000014-16001. References [1] Braunwald, Braunwald’s heart disease, in: P. Libby, R.O. Bonow, D.L. Mann, D.P. Zipes (Eds.), A Textbook of Cardiovascular Medicine, Saunders Elsevier, 2007. [2] S.J. Sarnoff, E. Berglund, Ventricular function: I. Starling’s law of the heart studied by means of simultaneous right and left ventricular function curves in the dog, Circulation 9 (1954) 706–718. [3] E.H. Starling, M.B. Visscher, The regulation of the energy output of the heart, J. Physiol. 62 (1926) 243–261. [4] S.W. Patterson, H. Piper, E.H. Starling, The regulation of the heartbeat, J. Physiol. 48 (1914) 357–379. [5] H. Pouleur, M.F. Rousseau, C. Van Eyll, J. Melin, M. Youngblood, S.A.L.I.M. 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