Improved identifiability of myocardial material parameters by an energybased cost function
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Abstract
Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometrybased CF is unable to uniquely constrain the parameter space. The energybased CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were \(C_1=3000\pm 1700\,\hbox {Pa}\) and \(\alpha =45\pm 25\) (\(b_f=25\pm 14\), \(b_{ft}=11\pm 6\), \(b_{t}=9\pm 5\)) for the HF cases and \(C_1=1700\,\hbox {Pa}\) and \(\alpha =15\) (\(b_f=8\), \(b_{ft}=4\), \(b_{t}=3\)) for the healthy case.
Keywords
Parameter estimation Myocardium Patientspecific modelling Passive constitutive equations1 Introduction
Left ventricular (LV) stiffness is proposed as a diagnostic indicator of cardiac function in heart failure (HF) patients (Westermann et al. 2008). Ventricular stiffness has been predominantly assessed in clinical practice through pressure–volume (p–V) analysis (Bermejo et al. 2013; Burkhoff et al. 2005; Zile et al. 2004). However, this approach is unable to distinguish between the anatomical and material contributions to LV stiffness. Specifically, an increment in ventricular size due to myocardial hypertrophy or an increase in collagen content with fibrosis may both lead to an equivalently stiffer LV behaviour using this methodology. Differentiating between these two components, anatomical and material, may improve the identification of HF aetiology in patients.
The development of biophysical models (Chen et al. 2016; Crozier et al. 2016; Krishnamurthy et al. 2013; Lee et al. 2015; Nordsletten et al. 2011; Plank et al. 2009) for the simulation of cardiac mechanics allows the distinct representation of the geometric and material components of stiffness. Using these models, the assessment of myocardial stiffness is posed as an inverse problem, where the material parameters are determined from known mechanical loads and deformations. Recent research in this field has focused on developing tractable pipelines for linking model parameters to data (Augenstein et al. 2005; Wang et al. 2009), evaluating the available material models (Criscione et al. 2002; Schmid et al. 2009) or improving the optimization strategies (Balaban et al. 2016; Moireau and Chapelle 2011; Moireau et al. 2008, 2009; Nair et al. 2007) and has led to the estimation of material parameters from clinical data sets (Asner et al. 2015; Gao et al. 2015; Wang et al. 2013; Xi et al. 2013). An inherent limitation in these current methods is the intercorrelation of the material parameters in myocardial material laws (Augenstein et al. 2006; Gao et al. 2015; Remme et al. 2004), which results in multiple parameter combinations corresponding to equivalent solutions in the optimization process. The existence of multiple solutions for the inverse problem limits the interpretation of these parameters for characterizing patient pathology and understanding changes in material properties under conditions of HF.
In this paper we investigate the role of the cost function (CF) in parameter identifiability and develop a novel energybased CF that allows us to uniquely constrain the myocardial material parameters. For our analysis we choose a popular material model in cardiac mechanics, the transversely isotropic constitutive equation proposed by Guccione et al. (1991), which is reported to suffer from parameter coupling (Augenstein et al. 2006; Xi et al. 2011). We examine how a group of CFs based on geometric attributes, and the energybased CF, constrain the optimization in the search of the parameters that best explain the clinical data of pressure and deformation. After an evaluation on a synthetic data set, a novel parameter estimation pipeline emerges based on the combined use of the energybased CF with one of the geometric CFs, and is tested in 8 clinical cases demonstrating its ability to identify unique material parameters from patient data.
2 Methods
In this section we summarize the synthetic and clinical data sets used (Sect. 2.1), the modelling framework (Sect. 2.2), the evaluated CFs (Sect. 2.3) and the proposed parameter estimation pipeline (Sect. 2.4). All data processing has been performed in MATLAB, and the meshes and simulation outputs have been visualized with cmGui ^{1} (Christie et al. 2002).
2.1 Data sets
2.1.1 Synthetic
To provide a known ground truth for the material parameters a synthetic data set was employed (see top panel in Fig. 1). An in silico model of the LV diastolic mechanics was created from the passive inflation of a truncated confocal prolate spheroidal of typical human cardiac dimensions representing the myocardial domain (Evangelista et al. 2011; Ho 2009; Humphrey 2002) to an enddiastolic pressure of 1.5 kPa (Humphrey 2002). A mesh of 320 (4 transmural, 16 circumferential, 4 longitudinal and 16 in the apical cap) hexahedral elements and 9685 nodes was used for the passive inflation simulation (details on the interpolation schemes and solver used are provided in Sect. 2.2.4). The pressure was applied over 30 equal pressure increments of 0.05 kPa, keeping the nodes of the ‘basal’ plane fixed in all directions. The resulting 31 meshes (undeformed mesh and 30 deformed meshes from each pressure increment) and their corresponding cavity pressure values from the simulation compose the synthetic data set used for the in silico study.
2.1.2 Clinical
The cardiac images of the CRT patient data sets (PC1–PC7) consist of 2D short axis stacks of cine MRI with SENSE encoding (\(1.19 \times 1.19 \times 8~\hbox {mm}^3\) to \(1.45 \times 1.45 \times 10~\hbox {mm}^3\) resolution), taken on a 1.5T—in six out of seven cases—or 3T—in one case—Achieva Philips Medical Systems MRI scanner. Each MRI sequence had 25 to 35 frames with a temporal resolution between 23 and 32 msec. The LV domain was manually segmented in itksnap ^{2} from the enddiastolic frame. Images were processed using a nonrigid registration (Shi et al. 2013) which enables a spatially and temporally continuous description of the cardiac motion. Mesh personalization was performed on the segmented LV, as described previously (Lamata et al. 2014). A set of deforming finite element (FE) meshes, consisting of 12 to 16 (4 circumferential, 3 to 4 longitudinal and 1 transmural) cubic hexahedral elements and 436 to 580 nodes, were created for each patient by warping the personalized enddiastolic anatomical mesh using the motion field corresponding to each frame of the cine MRI. As a result, correspondence of material points between frames is obtained from the cine MRI images through the image registration and mesh personalization processes.
The LV cavity pressure transient was recorded during a catheterization procedure before the beginning of the CRT pacing protocols and separately from the MRI scans. For each patient an average pressure trace was calculated over 5–13 beats and then synchronized to the cavity volume trace estimated from the personalized FE meshes. The pressure–volume synchronization was based on the assumption that the inflection point in the pressure wave is approximately aligned with the R wave (acquisition time of the first frame of each MRI sequence) and finding the temporal offset that maximized the p–V loop area and was less than 5% of the RR interval. The pressure transient was subsequently offset to ensure a zero pressure at the MRI phase that corresponded to the approximated reference configuration for the finite elasticity analysis, described below. The schematic of the steps followed for the processing of the clinical data sets is shown in the lower panel of Fig. 1.
2.2 Mechanical model
LV diastolic passive inflation is simulated using large deformation mechanics assuming that deformation is driven principally by the LV pressure, the myocardium has homogeneous material properties, is incompressible, inertia or viscoelastic effects are negligible, that the LV is in a stable relaxed state in late diastole and that the right ventricle (RV), atria, pericardium and other neighbouring structures have secondary roles.
2.2.1 Cardiac microstructure
Summary of the patient cases (PC1PC7) and healthy data set (HC) used
Case  Age  Sex  EF (%)  ESV (ml)  EDV (ml)  EDP (kPa) 

PC1  61  M  13.5  266  307  2.59 
PC2  61  M  6.2  348  371  1.21 
PC3  70  M  19.5  174  216  4.44 
PC4  76  F  32.3  86  127  1.84 
PC5  57  F  19.3  214  265  2.99 
PC6  65  M  29.7  122  173  1.17 
PC7  39  M  19.7  176  219  2.98 
HC  36  M  \(^{\mathrm{a}}\)  \(^{\mathrm{a}}\)  134  1.89 
2.2.2 Material description
2.2.3 Reference configuration
The reference configuration represents an idealistic stress and strainfree geometry for the myocardium which is never reached within the cardiac cycle. For simplicity the LV geometry associated with the MRI frame corresponding to the minimum pressure was chosen as an approximation of the reference geometry.
2.2.4 Mechanical simulations and boundary conditions
The evaluation of the geometrybased CFs involves the performance of mechanical simulations where the LV was passively inflated to enddiastolic pressure applied on the endocardial surface of the LV mesh. The motion of the basal plane nodes was prescribed from the data, which for the case of the synthetic data set translates to maintaining a fully fixed basal plane. All boundary conditions (BCs) were applied in 30 equal increments. Figure 2 schematically shows where BCs are applied and how they are determined from the clinical data.
The finite elasticity problem was solved within a multifield variational principle approach, with incompressibility enforced through a Lagrange multiplier. Cubic and linear Lagrange interpolation were chosen for the displacement field and pressure, respectively (Hadjicharalambous et al. 2014). The mechanical simulations were performed in the CHeart ^{3} nonlinear FE solver following a Galerkin FE method (Lee et al. 2016).
2.3 Examined CFs and their evaluation
2.3.1 Methodology to assess CF performance
To assess the parameter identifiability provided by the geometric and energybased CFs, we computed the CF residual across the \(\alpha \)\(C_1\) parameter space. We then visualize the landscapes of the CF residuals, and locate the parameter subspaces that could potentially be identified as solutions to the inverse problem. This, for the case of the geometrybased CFs, requires the performance of mechanical simulations with parameter sweeps over \(C_1\) and \(\alpha \). Conversely, the energybased CF relies only on data analysis, as highlighted below.
2.3.2 Geometrybased CFs
 \(L^2\) displacement norm. The \(L^2\) displacement norm CF is estimated by comparing the displacements between the simulated displacement (\(u_{\mathrm{sim}}\)) and the clinically measured or synthetic data (\(u_{\mathrm{dat}}\)):$$\begin{aligned} \Delta \varvec{u}=\sqrt{\frac{\int _\varOmega {({\varvec{u}}_{\mathrm{sim}}{\varvec{u}}_{\mathrm{dat}})\cdot ({\varvec{u}}_{\mathrm{sim}}{\varvec{u}}_{\mathrm{dat}}) \, \mathrm {d}\Omega }}{\int _\varOmega \mathrm {d}\Omega }} \, . \end{aligned}$$(7)
 \(L^2\) strain norm. The \(L^2\) norm of the difference in Green–Lagrange strains between simulated (\({\varvec{E}}_{\mathrm{sim}}\)) and synthetic or clinical data (\({\varvec{E}}_{dat}\)):The L2 displacement and L2 strain norm CFs were estimated using 4 Gauss points per element direction. Increasing the Gauss points to 5 per element direction led to a maximum 5 10\(^{8}\) mm error for \(\Delta \varvec{u}\) and 2 10\(^{7}\) error for \(\Delta \varvec{E}\), which is well within the expected magnitude of error due to data noise.$$\begin{aligned} \Delta \varvec{E}=\sqrt{\frac{\int _\varOmega {({\varvec{E}}_{\mathrm{sim}}{\varvec{E}}_{\mathrm{dat}}):({\varvec{E}}_{\mathrm{sim}}{\varvec{E}}_{\mathrm{dat}}) \, \mathrm {d}\Omega }}{\int _\varOmega \mathrm {d}\Omega }} \, . \end{aligned}$$(8)
 Cavity Volume. The Cavity Volume CF (\(\Delta \mathscr {V}\)) describes the absolute difference between the LV cavity volumes in the clinical or synthetic data (\(\mathscr {V}_{\mathrm{dat}}\)) and model simulations (\(\mathscr {V}_{\mathrm{sim}}\)):$$\begin{aligned} \Delta \mathscr {V} =  \mathscr {V}_{\mathrm{sim}}\mathscr {V}_{\mathrm{dat}} \, . \end{aligned}$$(9)
 Wall Thickness. The wall thickness metric \(\Delta d_{WT}\) compares the average wall thickness at the equatorial nodes between the simulation (\(d_{WT}^{\mathrm{sim}}\)) and the data (\(d_{WT}^{\mathrm{dat}}\)):where \(n_1,\ldots ,n_n\) are the node pairs at the equator used for the wall thickness measurements.$$\begin{aligned} \Delta d_{WT} = \frac{\sum \nolimits _{n_1}^{n_n} d_{WT}^{\mathrm{sim}}d_{WT}^{\mathrm{dat}}}{n_n} \, , \end{aligned}$$(10)
 Apicobasal distance. The endocardial \(\Delta d_{\mathrm{ABendo}}\) (or epicardial \(\Delta d_{\mathrm{ABepi}}\)) apicobasal distance metrics estimate the average difference between the distance of the endocardial (or epicardial) basal nodes to the endocardial (or epicardial) apical node at the data \(d_{ABendo}^{dat}\) (or \(d_{\mathrm{ABepi}}^{\mathrm{dat}}\)) and simulation \(d_{\mathrm{ABendo}}^{\mathrm{sim}}\) (or \(d_{\mathrm{ABepi}}^{\mathrm{sim}}\)) meshes:$$\begin{aligned} \Delta d_{\mathrm{ABendo}}= & {} \frac{\sum \nolimits _{m_1}^{m_n} d_{\mathrm{ABendo}}^{\mathrm{sim}}d_{\mathrm{ABendo}}^{\mathrm{dat}}}{m_n}, \end{aligned}$$(11)where \(m_1,\ldots ,m_n\) are the basal nodes whose distance from the apex is calculated.$$\begin{aligned} \Delta d_{\mathrm{ABepi}}= & {} \frac{\sum \nolimits _{m_1}^{m_n} d_{\mathrm{ABepi}}^{\mathrm{sim}}d_{\mathrm{ABepi}}^{\mathrm{dat}}}{m_n}, \end{aligned}$$(12)
2.3.3 Energybased CF
In implementing the energy CF we select two time points. In the clinical study these correspond to diastolic frames (DF) of the MRI sequence. To avoid potential artefacts from slow decaying active tension we choose to use frames from the MRI that corresponded to the last two frames of end diastole (this choice is reviewed in Appendix 7). We define \(DF_2\) as the enddiastolic frame, and \(DF_1\) as the frame prior to \(DF_2\) (see also Fig. 2). For consistency, we also chose to use the last two ‘frames’ in the analysis of the synthetic dataset. These correspond to the last two increments of the simulation used to generate it (\(DF_1\) corresponds to the solution after inflation to 1.45 kPa and \(DF_2\) to 1.5 kPa ).
The internal energy \(W_{\mathrm{int}}\) is calculated solely from the Green–Lagrange strain field which is derived from the displacement field between the geometries of the DF under consideration and the MRI frame employed as the reference frame. This tensor field can be calculated directly from the image registration algorithm without any further requirement for mechanical simulations of the LV model.
The energybased CF is only dependent on the parameters in Q in the Guccione law. Assuming constant anisotropy ratios then allows the \(\alpha \) parameter (Eq. 3) to be uniquely inferred form the energybased CF.
2.4 Proposed parameter estimation workflow

Step 1. Estimate \(\alpha \) through minimization of the energybased CF from analysing the data.

Step 2. Perform mechanical simulations in order to optimize \(C_1\) from the \(\Delta \varvec{u}\) CF.
3 Results
3.1 Parameter estimation in the synthetic data set
To determine if geometric CFs or the energybased CF can constrain passive stiffness parameters, in the absence of data noise and under conditions of absolute model fidelity to the data, we evaluate the CF performance on a synthetic data set with baseline Guccione constitutive law parameters set to \(\alpha =30\), \(C_1=1000~\hbox {Pa}\), \(r_f=0.55\), \(r_{ft}=0.25\), \(r_{t}=0.2\) (Eqs. 1, 3).
3.1.1 Identifiability of the geometrybased CFs
The reported coupling between the \(C_1\)\(\alpha \) parameters (Xi et al. 2013) is confirmed for the \(L^2\) displacement norm (Fig. 4a) and extended for the remaining geometrybased CFs (Fig. 4b–f). Fitting an inverse exponential function to the parameters with the minimum residual for each CF reveals that the CF minimization contours are highly coincident (Fig. 6). This shows that for the in silico case the geometrybased CFs, independently or in combination, are unable to uniquely constrain the parameters of the Guccione law.
3.1.2 Identifiability of the energybased CF
The landscape of the energybased CF residual in the \(C_1\)\(\alpha \) parameter space is shown in Fig. 3. Due to its formulation the energybased CF is independent of the \(C_1\) parameter, as is evident by the fact that its minimization contour is parallel to the \(C_1\) axis and the minimum occurs for a unique value of \(\alpha \). Combining the energybased CF with the \(L^2\) displacement norm the ground truth \(C_1\), \(\alpha \) parameters of the synthetic dataset were recovered (Fig. 3).
3.2 Parameter estimation in the clinical data sets
Following the in silico analysis we investigated the CF performance in 8 clinical cases.
3.2.1 Evaluating Geometric CFs on Clinical Data
The energybased CF must be paired with a geometric CF to constrain both the \(C_1\) and \(\alpha \) parameters. To determine the geometric CF to pair with the energybased CF we evaluated the six proposed geometric CFs on the 8 clinical data sets. The identifiable parameter combinations for each CF for each clinical data set are presented in Fig. 5 as summary plots of the exponential fits to the CF residual minimization parameter contours. This figure confirms that the \(C_1\)\(\alpha \) parameter coupling exists in vivo for all the geometric CFs. However, the minimization contours are not always coincident in the clinical setting, with some of the CF producing discordant parameter solutions as in cases PC2 and PC7 in Fig. 5.
The \(L^2\) norm of displacements was selected as the geometric CF to pair with the energy CF, as it is based on a thorough global comparison of the agreement of the deformation field between model and data and consistently accorded well with the majority of the other geometric CFs across cases.
3.2.2 Identifiability of the energybased CF
The energybased CF was estimated for the 8 clinical data sets. Its independence on \(C_1\) is verified in clinical data, as the CF minimizing parameter combinations form a horizontal line parallel to the \(C_1\) axis (see Fig. 5c, where the fitted line to the minimum residual contour is overlain on top of the exponential fits to the geometrybased CF minimums).
3.2.3 Estimated Parameters from the proposed pipeline
Parameter estimation results from the application of the proposed pipeline to the clinical data sets
Case  \(\alpha _{\mathrm{sol}}\)  \(C_1\) (Pa)  \(\Delta \varvec{u}\) (mm) 

PC1  61  5300  0.95 
PC2  61  820  1.96 
PC3  66  1960  2.56 
PC4  5  4780  5.61 
PC5  24  3140  \(^{\mathrm{b}}\) 
PC6  29  1460  1.91 
PC7  66  3300  1.18 
HC  15  1700  3.35 
Estimated \(C_1\), \(\alpha \) parameters for human myocardium from previous studies
Case  \(C_1\) (Pa)  \(\alpha \)  \(\Delta \varvec{u}\) (mm) 

Human  
Healthy\(^{\mathrm{a}}\)  \(2000 ^{\mathrm{e}}\)  43  1.78 
Patient 1\(^{\mathrm{a}}\)  \(2000 ^{\mathrm{e}}\)  105  1.58 
Patient 2\(^{\mathrm{a}}\)  \(2000 ^{\mathrm{e}}\)  95  1.39 
Healthy\(^{\mathrm{b}}\)  \(3600 \pm 1200^{\mathrm{e}}\)  38  – 
HT\(^{\mathrm{b,c}}\)  \(12000 \pm 2600 ^{\mathrm{e}}\)  38  – 
NIHF\(^{\mathrm{b,d}}\)  \(11800 \pm 3400 ^{\mathrm{e}}\)  38  – 
3.3 Comparison with Previous Methods
Differences in stress calculated with parameters estimated by proposed method (A) and a previous one (Xi et al. 2013), where \(C_1\) was fixed at 2000 Pa (B)
Case  Mean \({\hat{S}_{ff}}^A{\hat{S}_{ff}}^B\) (Pa)  Standard deviation (Pa)  \({\alpha }^B\) 

Synth  31.7  102.8  17 
PC1  \(\)461.8  47103.7  142 
PC2  \(\)577767.5  34820092.3  25 
PC3  3127.3  52912.1  65 
PC4  \(\)7.3  284.9  10 
PC5  \(\)0.5  82.2  35 
PC6  32.6  114.1  22 
PC7  18.7  598  92 
HC  45.7  972.7  13 
4 Discussion
We have shown that unique identification of myocardial material parameters is possible with a suitable choice of the CF. To our knowledge, this is the first time that the two coupled parameters in the Guccione model have been uniquely constrained by clinical data; previously this issue was addressed by fixing part of the parameter set (Asner et al. 2015; Hadjicharalambous et al. 2015; Wang et al. 2013; Xi et al. 2013).
4.1 Identifiability by an energybased CF
The core methodological contribution of this work is the proposal of a CF that removes the parameter coupling. The energybased CF identifies \(\alpha \) due to its independence to the \(C_1\) parameter. Its accuracy was tested in silico, where it estimates the correct \(\alpha \) parameter and combined with the \(L^2\) displacement norm provides accurate estimates of the ground truth parameter values. Results in 8 real clinical data sets with the complete pipeline demonstrate that the CF is robust to the inherent noise in clinical data and finite model fidelity.
The novel energybased CF is also a data driven metric. Only the data of deformation (strain and cavity volume) and pressure are required to compute it, without the need of computational simulations or data assimilation pipelines. This has three main benefits. Firstly, the data derived deformation field employed in the CF is unaffected by the \(C_1\)\(\alpha \) coupling that arises from the simulation. Secondly, the computationally expensive search over the full parameter space involved in current data assimilation schemes has one dimension of the parameter space reduced since the \(\alpha \) parameter is fixed. Thirdly, the reduction of methodological complexity to obtain the \(\alpha \) parameter opens the possibility for a quicker and easier clinical adoption.
It is important to note that the energybased CF raises the demands on data quality and quantity, since it requires strain data of the entire myocardium at two time points during diastole and the pressure–volume information covering the filling phase of the cycle. The importance of data quality on parameter estimation is demonstrated in a sensitivity study, provided in Appendix 6. In the absence of high fidelity strain data it is possible to recast the energy CF in terms of a pressure CF. This allows unique parameter estimates from pressure and volume transient data alone. The efficacy of this approach is presented in Appendix 8.
In our calculations the external work is estimated using a pressure–volume approach (see Eq. 19) which is fully accurate for the case of a deformation field consistent to the passive inflation assumption we have adopted. However, the image driven Dirichlet boundary conditions applied on the basal plane in the clinical data sets contribute to external work. This contribution is quantified as a mean 5% of the elastic energy in the clinical cases based on forward simulations with the identified parameters.
The efficiency of the energybased CF was demonstrated for the Guccione material law, but can be extended to other exponential constitutive relations for reducing the material parameter redundancy by one, such as the Holzapfel–Ogden law (Holzapfel and Ogden 2009) as demonstrated in Appendix 9 or the pole–zero (Nash and Hunter 2000) among others.
4.2 Geometrybased CFs
Geometrybased CFs, and their combination, were not able to identify unique myocardial material parameters, agreeing with previous reports (Augenstein et al. 2006; Xi et al. 2011).
Note that while the geometric cost functions are based on a single frame, in contrast to the two frames used in the energy CF, the addition of an adjacent frame is not expected to improve the identifiability of parameters from geometric cost functions when working with clinical data due to the presence of noise that is sufficiently large to obscure the global minimum, as reported in Xi et al. (2013).
4.3 Parameter estimation workflow
The proposed parameter estimation pipeline lead to a unique estimation of the Guccione material parameters in the 8 clinical data sets analysed in this study, and with \(\Delta \varvec{u}\) residuals (Table 2) comparable to previously reported errors (Table 3). It is important to note that the set of kinematic BCs in this study was lighter (only constraining the base, and not also the apex as in Xi et al. (2013)), thus making the task of reproducing the clinical observation more challenging.
In the proposed methodology the unique \(C_1\) and \(\alpha \) parameters, where the coupling occurs (Xi et al. 2013), are found while fixing two of the less intercorrelated ratios \(r_f\), \(r_t\), \(r_{ft}\) (the third is bound by Eq. 6). Once \(C_1\) and \(\alpha \) are found, the ratios can be uniquely found (as reported in Xi et al. (2013)). The impact of a wrong initial choice of \(r_f\), \(r_t\), \(r_{ft}\) on the estimation of \(C_1\) and \(\alpha \) parameters was evaluated in the sensitivity study (Appendix 6) and was found to be relatively low.
An important remark in the methodology is the existence of challenges associated with the convergence of nonlinear mechanics solvers in incompressible applications (Land et al. 2015b). The lack of convergence can often obstruct the calculation of the geometry CF residual for the whole \(C_1\) range of interest under the known \(\alpha \). Knowing that the set of coupled parameters that lead to very similar minimum costs draw a line in the \(\alpha \)\(C_1\) logscale space (Xi et al. 2013) allows the problem to be reformulated as finding the parameters of this exponential line.
4.4 Model assumptions
There are a series of model assumptions which, although not affecting the contribution of the proposed pipeline to parameter identifiability, may have an impact on the parameters found.
One important factor determining diastolic filling is the residual active tension (AT), which is known to be present in diastole (Bermejo et al. 2013; Xi et al. 2013). In this study the parameters are estimated from late diastolic instants and the inclusion of an earlier frame suggested the presence of remaining AT as detailed in Appendix 7. Enddiastolic events, where the AT can be assumed to be limited and its contribution to the work estimation negligible, are the most suitable observations. Following this approach, any remaining AT at end diastole leads to an apparent increased myocardial stiffness (Asner et al. 2015), specifically in the fibre direction (Xi et al. 2013).
The most important element in the proposed methodology was revealed to be the choice of the reference frame (see Appendix 6), in concordance with previous studies (Xi et al. 2013). The reference geometry directly affects the observed myocardial stiffness as it dictates the measured strain under a given cavity pressure. In this study the LV geometry at the lowest pressure frame was chosen to describe this geometry following a popular approach (Asner et al. 2015; Gao et al. 2015; Land et al. 2012; Nikou et al. 2016; Wang et al. 2009) in order to simplify the workflow. Inclusion of a reference frame estimation (Krishnamurthy et al. 2013) can possibly enhance the parameter estimation in future applications.
Assumptions are also made regarding the definition of the myocardial microstructure and material. Conforming to the majority of FE studies in the field of cardiac mechanics, the myocardium is assumed to be incompressible although capillary and coronary flow are known to locally violate this assumption (Ashikaga et al. 2008; Yin et al. 1996). This assumption affects both the simulated deformation fields from the mechanics solver, as well as the novel energybased CF, where deformation due to cardiac perfusion (increment of volume during diastole) is assimilated to contribute to the tissue strain energy. Also in the definition of myocardial microstructure, the inclusion of a more realistic fibre field might improve the accuracy in the estimation of the projected strain components (Eq. 3) and thus that of the energybased CF (Eq. 18). Nevertheless, results in the sensitivity study (Appendix 6) suggest that the impact of this assumption is very small, in accordance to previous studies (Land et al. 2015a).
A central assumption in the modelling approach followed here is myocardial material homogeneity, which by reducing model complexity facilitates the parameter estimation procedure. However, this currently restricts the application of the proposed method to disease where this assumption is valid. Although rendering our method suitable for cardiac disease with localized stiffness alterations such as myocardial infarction is within our future plans, our workflow is readily suitable for applications on disease, such as dilated cardiomyopathy, diffuse fibrosis or heart failure with normal ejection fraction (HFnEF), where tissue properties are expected to be more homogeneous.
One last set of assumptions are needed to define the BCs of the model. First, a homogeneous pressure load is assumed to act on the endocardial boundary during ventricular filling. This is a reasonable simplification based on the reported cavity pressure variations in the literature (de Vecchi et al. 2014), and is commonly taken for computational efficiency (Gao et al. 2015; Hadjicharalambous et al. 2015; Mojsejenko et al. 2015; Nikou et al. 2016; Wang et al. 2013). However the impact of the RV, atria and pericardium on achieving more physiological deformations is known (Belenkie et al. 2001; Tyberg and Smith 1990; Williams and Frenneaux 2006), and thus the use of more advanced mechanical models (Fritz et al. 2014) is anticipated to improve model fidelity and therefore parameter estimation accuracy. We would also expect that more realistic BCs will enable the model to better reproduce the recorded myocardial deformation, thus leading to smaller residual \(\Delta \varvec{u}\)—and this should be especially beneficial in 3 of our cases (PC3, PC4 & HC, see Table 2).
4.5 Estimated parameters in vivo
In our results we found that in four out of seven patient data sets (PC1, PC3, PC5, PC7) both identified parameters were higher than those of the healthy data set, in two cases (PC2, PC6) only the \(\alpha \) parameter was increased while in one case (PC4) \(C_1\) was increased instead. The sample of 7 HF subjects already reveals functional differences by the linear and exponential components of the material law, and we can speculate that this may have links with the aetiology of the disease. While the general trend is for the HF patient data sets processed to have higher stiffness than the healthy case in agreement with previous clinical studies (Wang et al. 2013), further cases need to be investigated to confirm this observation.
4.6 Cardiac Mechanics Application
Computational models are increasingly used in clinical applications. Inferring material properties from these models offers three potential applications with each benefiting from uniquely constrained parameters. Firstly, material parameters may provide a more sensitive descriptor of patient pathology (Lamata et al. 2016). To test the utility of this application first requires a method for inferring constitutive parameters from clinical data. Assuming that these parameters reflect some underlying material property, and hence reflect the patients pathology, then the inferred parameter values should be unique and independent of the fitting method.
Secondly, the use of biophysical models, that are constrained by physical laws, increases the capacity of models to predict outside of the data used to constrain them. This is particularly important for patientspecific models that could be used to predict response to treatments from pre procedure data. The use of nonunique parameters will make these predictions dependent on the parameter inference method, increasing the uncertainty in the model predictions.
Finally, models can be used for predicting values of interest that can not easily be measured, including material stress, regional work and local mechanical efficiency. We have shown that an alternative approach for a unique estimation of parameters, by fixing \(C_1\), leads to significant discrepancy in model predictions (see Fig. 7).
5 Conclusions
A novel and clinically tractable pipeline for passive myocardial material estimation is proposed, which manages to decouple the material constitutive law parameters and guarantee reliable material estimation. This is an important step towards the use of myocardial stiffness as a reliable tool for the understanding of cardiac pathophysiology and the development of biomechanically relevant biomarkers. This work highlights the central role of CFs in the identifiability of material parameters.
Footnotes
Notes
Acknowledgements
This work was supported by the British Heart Foundation (PG/13/37/30280), the UK Engineering and Physical Sciences Research Council (EPSRC) (EP/M012492/1), the King’s College London Wellcome Trust and EPSRC Medical Engineering Centre and the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust. PL holds a Sir Henry Dale Fellowship funded jointly by the Wellcome Trust and the Royal Society (Grant No. 099973/Z/12/Z). SN has received support from Boston Scientific and St Jude Medical. CAR receives research funding and Honoraria from St Jude Medical, Medtronic, and Boston Scientific.
Conflict of interest
The authors declare that they have no other conflicts of interest.
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