 Research
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Comparisons of simulation results between passive and active fluid structure interaction models for left ventricle in hypertrophic obstructive cardiomyopathy
BioMedical Engineering OnLine volume 20, Article number: 9 (2021)
Abstract
Background
Patientspecific active fluid–structure interactions (FSI) model is a useful approach to noninvasively investigate the hemodynamics in the heart. However, it takes a lot of effort to obtain the proper external force boundary conditions for active models, which heavily restrained the timesensitive clinical applications of active computational models.
Methods
The simulation results of 12 passive FSI models based on 6 patients’ preoperative and postoperative CT images were compared with corresponding active models to investigate the differences in hemodynamics and cardiac mechanics between these models.
Results
In comparing the passive and active models, it was found that there was no significant difference in pressure difference and shear stress on mitral valve leaflet (MVL) at the preSAM time point, but a significant difference was found in wall stress on the inner boundary of left ventricle (endocardium). It was also found that pressure difference on the coapted MVL and the shear stress on MVL were significantly decreased after successful surgery in both active and passive models.
Conclusion
Our results suggested that the passive models may provide good approximated hemodynamic results at 5% RR interval, which is crucial for analyzing the initiation of systolic anterior motion (SAM). Comparing to active models, the passive models decrease the complexity of the modeling construction and the difficulty of convergence significantly. These findings suggest that, with proper boundary conditions and sufficient clinical data, the passive computational model may be a good substitution model for the active model to perform hemodynamic analysis of the initiation of SAM.
Background
Hypertrophic obstructive cardiomyopathy (HOCM) is characterized by hypertrophic myocardium and obstruction in the left ventricular outflow. Patients with this disease might suffer from severe heart failure and even sudden death. Septal myectomy is the golden standard treatment for the symptomatic patients [1, 2]. However, this is a very challenging procedure as the extent of myectomy is very difficult to be determined. This is because that inadequate excision cannot abolish left ventricular outflow obstruction, while the excessive myectomy might produce ventricular septal defect or irregular heart rhythms, such as complete heart block. Therefore, a noninvasive method for helping surgeons make the optimal design of the surgery is significantly required.
Several heart models, such as structural finite element models, computational fluid dynamics models, fluid–structure interactions (FSI) models, multipatient models, have been proposed in the literature to assess the hemodynamics and myocardial functions in heart and become increasingly important in cardiovascular research [3,4,5,6,7,8,9,10,11,12,13]. Cardiac tissue is generally modeled as a hyperelastic material [14]. Heart models can be divided into passive and active models to model the myocardial behavior [8, 15,16,17,18,19,20,21,22,23]. The incompressible elastic solids are generally used to model soft tissue in passive models. The exponential strainenergy functions have been widely used to describe the mechanical behavior of passive myocardium [15, 18]. The Holzapfeltype energy functions were commonly used for passive tissue stress [9, 24,25,26]. Electrical activation is one way to trigger the cardiac active contraction [8, 16, 17, 19,20,21]. While this method offers much more insight into the physiological processes, it also requires patientspecific identification of all the parameters inside the electromechanical models. In our case, the left ventricle (LV) is a severely pathological LV which may differ considerably from the ones reported in literature for healthy patients. Applying timedependent material properties in a solid model is also a method to model the active contraction [22, 27]. However, patientspecific timedependent material properties were not able to be measured at the in vivo state due to current technological limitations. In our study, we applied an external force to implement the LV myocardial active contraction. The 3D active FSI models were applied to investigate the biomechanics of the myocardium and the intraventricular flow in LV [28]. However, it takes a lot of effort to obtain the proper external force boundary conditions to match the numerical simulation results with clinical data, which prevents the active computational models from timesensitive clinical applications. Comparing to the active model, the pressure boundary conditions of passive models were easier to obtain. Additionally, it’s easier to obtain the convergent solutions of passive model. In this study, we constructed 12 patientspecific computedtomography (CT)based passive FSI models for the LV of patients with HOCM, and we compared the numerical simulation results between these passive models and their corresponding active FSI models to investigate the differences.
Results
Based on 6 patients’ preoperative and postoperative LV CT Images, 12 patientspecific active and corresponding passive FSI models were constructed in this study. Figure 1 presents the numerical simulation results of stressP_{1} on the cutting surface, 3D velocity vector flow map, pressure difference, and shear stress distribution on the MVL at PreSAM time point obtained from preoperative active and passive FSI models (patient 1). Figure 1 shows that the maximum stress value on the cutting surface obtained from passive models were 41.8% higher than that from active models, while the differences between fluid results were not significant. Details of the model comparisons are given below.
Maximum wall stress values in passive models were 29.09% higher than those in active model
Figure 2 presents the boxplots for the wall stress values obtained from slices in preoperative and postoperative active and passive models. The mean values of maximum wall stress of each active model were higher than those obtained from corresponding passive model (Fig. 2).
The maximum/minimum/mean values of wall stress and wall strain of each patient obtained from active models were compared with those from passive models. The comparison results are presented in Table 1. The mean value of maximum wall stress values in passive models were found 29.09% higher than that in active models (122.7 ± 38.1 kPa vs. 95.0 ± 26.9 kPa, p = 0.04). No significant difference was found in maximum wall strain values between passive and active models (0.106 ± 0.015 vs. 0.112 ± 0.019, p = 0.39).
No significant differences were found in fluid results
No significant differences between active and passive models were found in mean values of maximum pressure values and shear stress on MVL (pressure: 82.3 ± 11.7 kPa vs. 83.3 ± 12.0 kPa, p = 0.82; shear stress: 12.1 ± 6.8 dyn/cm^{2} vs. 11.6 ± 6.4 dyn/cm^{2}, p = 0.85). The details of comparisons results of mean pressure and shear stress values are also listed in Table 2.
Pressure difference and shear stress on MVL were found significantly decreased after successful surgery in both of active and passive models
It was noticed that, before the surgery, the pressure on the posterior surface of MVL was higher than that on the anterior surface [28]. Therefore, to better investigate the differences between active and passive models, the pressure difference between posterior and anterior surfaces of MVL of the patients before and after surgery between different groups (patients receiving successful surgery vs. failed surgery) were investigated (Table 3). It was found that the pressure difference on MVL obtained from passive model decreased significantly after septal myectomy in satisfactoryoutcome group (maximum pressure difference, 2.44 ± 1.30 mmHg vs. 0.68 ± 0.29 mmHg, p = 0.017; mean pressure difference, 1.17 ± 0.68 mmHg vs. 0.24 ± 0.14 mmHg, p = 0.024).
Although the values of pressure differences between posterior and anterior surfaces of MVL in passive models were lower than those obtained from active models, the same observations were found from both models (Fig. 3).
The preoperative and postoperative shear stress on MVL obtained from active and passive models were also investigated (Fig. 4). It was observed that the max shear stress on MVL decreased 52.1% (p = 0.01) in active models and 49.5% (p = 0.004) in passive models after successful surgical septal myectomy. The mean shear stress was found decreased significantly in both of active models (preop: 6.38 ± 1.76 dyn/cm^{2} vs. postop: 3.27 ± 1.30 dyn/cm^{2}, p = 0.004) and passive models (preop: 5.85 ± 1.41 dyn/cm^{2} vs. postop: 3.07 ± 1.22 dyn/cm^{2}, p = 0.004) (Table 3). The postoperative maximum shear stress on MVL for the patient receiving failed surgery were 259% (active models, 37.0 dyn/cm^{2} vs. 10.3 dyn/cm^{2}) and 277% (passive models, 35.8 dyn/cm^{2} vs. 9.5 dyn/cm^{2}) higher than the mean value of those in group 1 (Table 3).
Discussion
In this study, there were 12 active and corresponding passive FSI models constructed to compare the differences of simulation results between these two types of models. The numerical simulation results at preSAM time point, including wall stress and strain on the inner boundary of LV, and pressure and shear stress on MVL, were extracted to be compared. It was found that there was a significant difference in wall stress between the passive and active models. The mean value of maximum wall stress in passive models was 29.09% higher than that in active model. However, there were no significant differences of strain values and fluid results between the active and passive models. Based on simulation results obtained from passive models, the pressure difference on coapted MVL and shear stress on MVL were found decreased significantly after successful surgery, but remained still high after failed surgery. These findings are in good agreement with the results obtained from active models [28].
While the mechanisms driving the left ventricle motion in the passive model are different from real heart motion, the simulation of the hemodynamic status, the motion and deformation of LV can still be approached by the passive models. In both of our passive and active models, the boundary conditions were adjusted to match the simulation results with clinical data. That is, at preSAM time, the simulation results of average pressure, LV volume, and LVOT velocity were matched with clinical measured data well in both of passive and active models. Our results indicated that, if we focus on the simulation results of left ventricle at some specific time points, the passive model may provide simulation results of blood flow matching well with active models with proper boundary conditions and sufficient clinical data.
Table 4 presents the number of manually adjustments of simulation experiments for passive model and active models. The total number of numerical simulation experiments for active model to receive the proper boundary conditions was about two times more than that of passive models (\(15.8\pm 6.6\) vs. \(5.2\pm 1.6\)). Every simulation experiment takes around 3 h. Therefore, for each FSI model, it took at least more than 30 h to obtain the proper boundary conditions for active models comparing to passive models. Suppose the researcher works 6 h/day, 5 weekday/week. Which means the patient needs around one more week to receive the simulation results and surgical plan. In addition, to obtain the convergent solutions, for every simulations, the active models needs 85% more iteration times than passive models (active vs. passive, 12.3 \(\pm 3.3\) vs. 6.7 \(\pm 0.7\)). The passive models also decrease the difficulty of convergence significantly. Since the goal of our study was to perform hemodynamic analysis of LV to investigate the mechanisms of SAM, and the passive models provided good approximated hemodynamic simulations, the passive models may be used as a good substitution model to active models to investigate the initiation of SAM.
Some limitations of this study are acknowledged here. The main limitation of the study is the small sample size which results in limited statistical power. The reason for the small sample size is that the computational modeling method takes large time costs, (1) it takes a lot of effort to find the proper boundary conditions for active models; (2) the construction procedure for both of active and passive models also takes time; (3) it is not easy to obtain patientspecific CT data, especially for the follow up CT data. Currently, multipatient studies for heart model simulations is still rare. Adding more patients will be our future effort. Applying commercial software, such as HyperMesh, to automatically create the finite element mesh to save the construction time will be considered. Although CT images provided highresolution medical images, the chordae tendineae is still impossible to be displayed. Therefore, the chordae tendineae is not included in our model. Due to rapid motion, our technique also restricts us from simulating mitral valve dynamic motions. From the end of the isovolumic systole to the preSAM time point, the mitral valve is almost closed, therefore, this simplification would have little impact on fluid results for analyzing the mechanisms of initiation of SAM. It should also be noted that, the fibre orientation plays a critical role both in passive inflation [26, 29, 30] and active contraction [31, 32]. The anisotropic models may improve the computational prediction accuracies. However, in the heart in HCM patients, the alignment of muscle cells or myocardial disarray was found very irregular and disorganized [33, 34]. The fiber orientation data were not able to be obtained with current technology. Therefore, we assumed that the material of the left ventricle was isotropic. In the future, the single or multilayer anisotropic models would be introduced into our model to investigate other patients’ left ventricle models.
Conclusion
In this study, based on CT images of 6 HOCM patients before and after surgical septal myectomy, 12 active and corresponding passive FSI models were developed to compare the simulated biomechanics and hemodynamics behaviors between passive and active models. It was found that, between these models, there was a significant difference in wall stress, but the differences in hemodynamic simulation results were not significant. Compared with active models, the passive models decrease the complexity of the modeling construction and the difficulty of convergence significantly. Therefore, with proper boundary condition, the passive model may be a good approximation to active model with less computational cost to perform hemodynamic analysis for left ventricle to investigate the initiation of SAM. Prospective and largescale studies are needed to further validate our findings.
Methods
Data acquisition
Institution review board approval on human subject research at Fuwai Hospital was obtained for this study. For model constructions and analysis, the CT images before and after surgery were obtained from six patients with HOCM who received septal myectomy in Fuwai Hospital. All these six patients had severe systolic anterior motion (SAM) before surgery. Based on the outcomes of surgery, patients were categorized into two groups, satisfactoryoutcome group (patient no. 1–5) which successfully eliminated SAM and mitral regurgitation, and unsatisfactoryoutcome group (patient no. 6) which had residual SAM and mitral regurgitation after surgery. Preoperative and postoperative echocardiographic characteristics are summarized in Table 5.
Cardiac CT images were obtained at every 5% interbeat (RR) interval. In all these six patients, the beginning time of SAM ranged from 5 to 8% RR interval in the cardiac cycle. Therefore, to investigate the initiation of SAM, the preoperative and postoperative CT images at 5% RR interval were selected as preSAM time point data to construct the computational models. The field of view was 256 mm × 256 mm, the matrix was 512 × 512, and the slice thickness was 0.625 mm. The original slice thickness of the CT images was 0.625 mm, and there were about 110–150 slices of images covering the LV. The segmentation was performed manually by Dr. Deng to obtain digital contours of each component for modeling constructions. The segmentation results were all examined by experienced radiologist. Volume componentfitting method was employed to generate meshes for left ventricle with irregular geometry. Using this technique, both the left ventricle and fluid domains were divided into thousands of small “volumes” to curvefit the geometry. The edge of each volume will be further divided into several divisions to generate the final mesh in ADINA (ADINA R&D, Watertown, MA). By applying this method, the mesh generated would not be too distorted under large deformation. More details about the CT images and segmentation for the construction of FSI models can be found from Deng et al. [28, 35]. Patients’ heart rate and blood pressure at the time of CT examinations were used in the simulation. The patientspecific LV volume, pressure and the left ventricular outflow tract (LVOT) velocity at preSAM time obtained from echo and MRI data were used to verify the simulation results (Table 6).
Solid models
The modified nonlinear Mooney–Rivlin model was adopted to characterize the mechanical behavior of LV myocardium. The strainenergy function for the isotopic incompressible Mooney–Rivlin model was expressed as,
where \({I}_{1}\), and \({I}_{2}\) are the first and second strain invariants,
\({\varvec{C}}=\left[{C}_{ij}\right]={{\varvec{X}}}^{\mathrm{T}}{\varvec{X}}\) is the right CauchyGreen deformation tensor, \({\varvec{X}}=\left[{X}_{ij}\right]=[\partial {x}_{i}/\partial {a}_{j}]\), where \({x}_{i}\) is the current position, \({a}_{j}\) is the original position, and \({c}_{1}\),\({c}_{2}\), \({D}_{1}\), and \({D}_{2}\) are material constants [27, 36]. Details of the determination of the patientspecific material constants have been described in our previous publication [28, 35].
Fluid dynamics simulation
The blood in the LV was treated as a laminar, Newtonian, viscous and an incompressible fluid. In this study, we set the viscosity of \(\mu =0.04\mathrm{ dyn }{\mathrm{cm}}^{2}\) and density of \(\rho =1\mathrm{ g }{\mathrm{cm}}^{3}\) for blood properties. The Navier–Stokes equation with Arbitrary Lagrangian Eulerian formula was used as the governing equation. Boundary conditions in the simulation were set such that, during the ejection phase, when blood was ejected out of the left ventricle, the outlet (aortic valve) kept open and the inlet (mitral valve) was closed (flow velocity was set to zero, and the pressure was left unspecified). A noslip boundary condition between the interfaces was assumed. The structure and fluid models were coupled through their interfaces [6, 27, 36]. The complete fluid model is given by,
where \({\varvec{u}}\) and \(p\) are fluid velocity and pressure, \({{\varvec{u}}}_{g}\) is mesh velocity, \(\Gamma \) stands for the left ventricle inner wall, \({\varvec{\sigma}}\) is structure stress tensor, (superscripts, f and s, indicate blood and the left ventricle, respectively), and \({\varvec{n}}\) is the outward normal directions. The governing equations of (Eqs. (3–4)) would be rewritten to weak form by using Galerkin method [36]. Then, interpolation functions would be introduced for each element, and finite element governing equations are expressed in terms of interpolation functions. Difference method and Newton–Raphson iteration method were also used to obtain the solution iteratively.
Solution method
In the construction process of both of active and passive models, the initial shrinkage rate was set as 5% to receive the zeroload geometry which is the starting geometry for the numerical simulation [37, 38]. When the pressure was applied at inlet, the left ventricle expanded in shortaxis and longaxis direction, and then regained its in vivo morphology. During this phase, the mitral valve (MV) kept open and the blood filled into LV though MV. At the end of this phase, LV reaches its maximum volume and the pressure in LV increases to end of isovolumic systole pressure. The information of the blood flow and mechanics of LV were received as the starting state of the simulation for the phase from end of systole phase to preSAM time point. The details of the preshrink process can be found from our previously published paper [28, 35].
The finite element meshes was generated by the volume componentfitting method [6, 27]. The constructed computational models were solved by commercial software ADINA (ADINA R&D, Watertown, MA), which uses the Newton–Raphson iteration and unstructured finite elements method. The iterative FSI coupling solution method, which requires less memory than the Direct FSI Coupling method, was applied to handle fluid–structure interaction. At each time step, the fluid and solid equations will be solved individually where the latest information provided by the other part is used as boundary conditions at each time step. Specifically speaking, the solid model firstly will be solved with the latest pressure and stress condition provided by the flow part of the last time step. Then, the fluid model will be solved by using the displacement and velocity obtained from the structure part as the boundary conditions. These two steps will be repeated until the convergence is reached. Mesh analysis was performed for each model by reducing the mesh density in each dimension by 10% until differences between solutions from two consecutive meshes were negligible (less than 1% in L2norm). The optimal element size in each dimension is between 0.05 and 0.1 cm. For each patient, there were around 90,000 elements and 60,000 elements for the solid and fluid model, respectively.
Differences between the passive and active models
The simulations of the phase from end of systole phase to preSAM time point between active model and passive model were different. In passive models, the left ventricle muscle was deflated by the patientspecific pressure condition which was scaled based on measured blood pressure conditions, and the pressure difference between LV and aorta which was obtained from ultrasound scanning. In active models, we simulated the LV active contraction by specifying the external forces on epicardium, and the pressure condition at outlet (aortic valve) from the end of isovolumic systole phase to preSAM time point (5% RR) [28]. Based on the clinical measured data of LV volume, LV pressure, and LVOT velocity obtained from echo and MRI data at the end of isovolumic systole phase and preSAM time point, the external forces were applied on each element of the epicardium, and the pressure condition at outlet at each time step were numerically interpolated. The boundary conditions were manually adjusted accordingly, such that the difference between clinical measured data and the simulation results were less than 5%. The real LV active contraction motion was then implemented. The adjustment of the boundary conditions was primarily based on the comparisons of numerical results and clinical data at each time step. For more details about the construction of active and passive models, we refer the reader to our previous publications [28].
Data extraction and statistical analysis
The stress, strain, and shear stress are all tensors, therefore, the maximum principal stress (stressP_{1}), maximum principal strain (strainP_{1}), and maximum shear stress at each node were chosen as wall stress, strain, and shear stress, respectively, for convenience. Data for wall stress and wall strain of all integral nodes on the inner boundary of left ventricle (endocardium) and fluid shear stress and pressure of all integral nodes on mitral valve were extracted from 3D FSI solutions. For each slice, the maximum/mean/minimum values of the simulation results of wall stress, wall strain, fluid shear stress, and pressure were selected for analysis. Which means, there were at least 20 samples come from the same patient (but comes from different slice). The data were in nonindependent data structure. Therefore, the linear mixedeffectmodel [39, 40] was used to compare the simulation results between passive and active models. The statistical significance was established at a p value of < 0.05. All statistical analyses in this study were conducted using R software (version 3.5.1).
Availability of data and materials
The datasets generated during and/or analyzed during the current study are available from the corresponding author at reasonable request.
Abbreviations
 FSI:

Fluid–structure interactions
 MVL:

Mitral valve leaflet
 SAM:

Systolic anterior motion
 HOCM:

Hypertrophic obstructive cardiomyopathy
 LV:

Left ventricle
 CT:

Computedtomography
 RR:

Interbeat
 LVOT:

Left ventricular outflow tract
 MV:

Mitral valve
 StressP_{1} :

Maximum principal stress
 StrainP_{1} :

Maximum principal strain
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Acknowledgements
The authors thank Xinshuang Ren for collecting the CT data. Bin Lyu performed the cardiac CT test. Fujian Duan performed the echocardiography.
Funding
The research for this project is funded in part by the Natural Science Foundation of Fujian Province of China Grant 2017J01009, the Fundamental Research Funds for the Central Universities Grant 20720180004, the National Natural Sciences Foundation of China Grants 11672001, 82000472, 11671335, and 11972117, and the NIH/NHLBI R01 HL089269 (Dr. Tang).
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XH and LD designed the study. YS performed the operations. LD collected the clinical data. CY constructed the computational model and performed the numerical simulation. LD and XH wrote the paper. HZ, ML, and XH perform the statistical analysis. YS, ML, and DT critically reviewed the paper. All authors read and approved the final manuscript.
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This study was approved by Institution review board approval on human subject research at Fuwai Hospital.
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The authors declare that they have no competing interests.
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Huang, X., Deng, L., Zuo, H. et al. Comparisons of simulation results between passive and active fluid structure interaction models for left ventricle in hypertrophic obstructive cardiomyopathy. BioMed Eng OnLine 20, 9 (2021). https://doi.org/10.1186/s12938020008384
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DOI: https://doi.org/10.1186/s12938020008384
Keywords
 Fluid–structure interactions
 Left ventricle
 Mitral valve
 Systolic anterior motion
 Passive computational model
 Active computational model