Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.

Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function.

BACKGROUND: Preterm birth leads to an early switch from fetal to postnatal circulation before completion of left ventricular in utero development. In animal studies, this results in an adversely remodeled left ventricle. We determined whether preterm birth is associated with a distinct left ventricular structure and function in humans. METHODS AND RESULTS: A total of 234 individuals 20 to 39 years of age underwent cardiovascular magnetic resonance. One hundred two had been followed prospectively since preterm birth (gestational age=30.3±2.5 week; birth weight=1.3±0.3 kg), and 132 were born at term to uncomplicated pregnancies. Longitudinal and short-axis cine images were used to quantify left ventricular mass, 3-dimensional geometric variation by creation of a unique computational cardiac atlas, and myocardial function. We then determined whether perinatal factors modify these left ventricular parameters. Individuals born preterm had increased left ventricular mass (66.5±10.9 versus 55.4±11.4 g/m(2); P<0.001) with greater prematurity associated with greater mass (r = -0.22, P=0.03). Preterm-born individuals had short left ventricles with small internal diameters and a displaced apex. Ejection fraction was preserved (P>0.99), but both longitudinal systolic (peak strain, strain rate, and velocity, P<0.001) and diastolic (peak strain rate and velocity, P<0.001) function and rotational (apical and basal peak systolic rotation rate, P =0.05 and P =0.006; net twist angle, P=0.02) movement were significantly reduced. A diagnosis of preeclampsia during the pregnancy was associated with further reductions in longitudinal peak systolic strain in the offspring (P=0.02, n=29). CONCLUSIONS: Individuals born preterm have increased left ventricular mass in adult life. Furthermore, they exhibit a unique 3-dimensional left ventricular geometry and significant reductions in systolic and diastolic functional parameters. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01487824.

A prospective evaluation of cardiovascular magnetic resonance measures of dyssynchrony in the prediction of response to cardiac resynchronization therapy.

BACKGROUND: Many patients with electrical dyssynchrony who undergo cardiac resynchronization therapy (CRT) do not obtain substantial benefit. Assessing mechanical dyssynchrony may improve patient selection. Results from studies using echocardiographic imaging to measure dyssynchrony have ultimately proved disappointing. We sought to evaluate cardiac motion in patients with heart failure and electrical dyssynchrony using cardiovascular magnetic resonance (CMR). We developed a framework for comparing measures of myocardial mechanics and evaluated how well they predicted response to CRT. METHODS: CMR was performed at 1.5 Tesla prior to CRT. Steady-state free precession (SSFP) cine images and complementary modulation of magnetization (CSPAMM) tagged cine images were acquired. Images were processed using a novel framework to extract regional ventricular volume-change, thickening and deformation fields (strain). A systolic dyssynchrony index (SDI) for all parameters within a 16-segment model of the ventricle was computed with high SDI denoting more dyssynchrony. Once identified, the optimal measure was applied to a second patient population to determine its utility as a predictor of CRT response compared to current accepted predictors (QRS duration, LBBB morphology and scar burden). RESULTS: Forty-four patients were recruited in the first phase (91% male, 63.3 ± 14.1 years; 80% NYHA class III) with mean QRSd 154 ± 24 ms. Twenty-one out of 44 (48%) patients showed reverse remodelling (RR) with a decrease in end systolic volume (ESV) ≥ 15% at 6 months. Volume-change SDI was the strongest predictor of RR (PR 5.67; 95% CI 1.95-16.5; P = 0.003). SDI derived from myocardial strain was least predictive. Volume-change SDI was applied as a predictor of RR to a second population of 50 patients (70% male, mean age 68.6 ± 12.2 years, 76% NYHA class III) with mean QRSd 146 ± 21 ms. When compared to QRSd, LBBB morphology and scar burden, volume-change SDI was the only statistically significant predictor of RR in this group. CONCLUSION: A systolic dyssynchrony index derived from volume-change is a highly reproducible measurement that can be derived from routinely acquired SSFP cine images and predicts RR following CRT whilst an SDI of regional strain does not.

Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology.

Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.

An automatic service for the personalization of ventricular cardiac meshes.

Computational cardiac physiology has great potential to improve the management of cardiovascular diseases. One of the main bottlenecks in this field is the customization of the computational model to the anatomical and physiological status of the patient. We present a fully automatic service for the geometrical personalization of cardiac ventricular meshes with high-order interpolation from segmented images. The method is versatile (able to work with different species and disease conditions) and robust (fully automatic results fulfilling accuracy and quality requirements in 87% of 255 cases). Results also illustrate the capability to minimize the impact of segmentation errors, to overcome the sparse resolution of dynamic studies and to remove the sometimes unnecessary anatomical detail of papillary and trabecular structures. The smooth meshes produced can be used to simulate cardiac function, and in particular mechanics, or can be used as diagnostic descriptors of anatomical shape by cardiologists. This fully automatic service is deployed in a cloud infrastructure, and has been made available and accessible to the scientific community.

Share and enjoy: anatomical models database--generating and sharing cardiovascular model data using web services.

Sharing data between scientists and with clinicians in cardiac research has been facilitated significantly by the use of web technologies. The potential of this technology has meant that information sharing has been routinely promoted through databases that have encouraged stakeholder participation in communities around these services. In this paper we discuss the Anatomical Model Database (AMDB) (Gianni et al. Functional imaging and modeling of the heart. Springer, Heidelberg, 2009; Gianni et al. Phil Trans Ser A Math Phys Eng Sci 368:3039-3056, 2010) which both facilitate a database-centric approach to collaboration, and also extends this framework with new capabilities for creating new mesh data. AMDB currently stores cardiac geometric models described in Gianni et al. (Functional imaging and modelling of the heart. Springer, Heidelberg, 2009), a number of additional cardiac models describing geometry and functional properties, and most recently models generated using a web service. The functional models represent data from simulations in geometric form, such as electrophysiology or mechanics, many of which are present in AMDB as part of a benchmark study. Finally, the heartgen service has been added for producing left or bi-ventricle models derived from binary image data using the methods described in Lamata et al. (Med Image Anal 15:801-813, 2011). The results can optionally be hosted on AMDB alongside other community-provided anatomical models. AMDB is, therefore, a unique database storing geometric data (rather than abstract models or image data) combined with a powerful web service for generating new geometric models.

Model-based vasculature extraction from optical fluorescence cryomicrotome images.

The aim of this study was to develop a novel method to reconstruct 3-D coronary vasculature from cryomicrotome images, comprised of two distinct sets of data-fluorescent microsphere beads and coronary vasculature. Fluorescent beads and cast injected into the vasculature were separately imaged with different filter settings to obtain the microsphere and vascular data, respectively. To extract the vascular anatomy, light scattering in the tissue was modelled using a point spread function (PSF). The PSF was parametrized by optical tissue excitation and emission attenuation coefficients, which were estimated by fitting simulated images of microspheres convolved with the PSF model to the experimental microsphere images. These parameters were then applied within a new model-based method for vessel radius estimation. Current state-of-the-art radii estimation methods and the proposed model-based method were applied on vessel phantoms. In this validation study, the full-width half-maximum method of radii estimation, when performed on the raw data without correcting for the optical blurring, resulted in 42.9% error on average for the 170 µm vessel. In comparison, the model-based method resulted in 0.6% error on average for the same phantom. Whole-organ porcine coronary vasculature was automatically reconstructed with the new model-based vascular extraction method.

The estimation of patient-specific cardiac diastolic functions from clinical measurements.

An unresolved issue in patients with diastolic dysfunction is that the estimation of myocardial stiffness cannot be decoupled from diastolic residual active tension (AT) because of the impaired ventricular relaxation during diastole. To address this problem, this paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastolic residual AT. Dynamic C1-continuous meshes are automatically built from the anatomy and deformation captured from dynamic MRI sequences. Diastolic deformation is simulated using a mechanical model that combines passive and active material properties. The problem of non-uniqueness of constitutive parameter estimation using the well known Guccione law is characterized by reformulation of this law. Using this reformulated form, and by constraining the constitutive parameters to be constant across time points during diastole, we separate the effects of passive constitutive properties and the residual AT during diastolic relaxation. Finally, the method is applied to two clinical cases and one control, demonstrating that increased residual AT during diastole provides a potential novel index for delineating healthy and pathological cases.

Myocardial perfusion distribution and coronary arterial pressure and flow signals: clinical relevance in relation to multiscale modeling, a review.

Coronary artery disease, CAD, is associated with both narrowing of the epicardial coronary arteries and microvascular disease, thereby limiting coronary flow and myocardial perfusion. CAD accounts for almost 2 million deaths within the European Union on an annual basis. In this paper, we review the physiological and pathophysiological processes underlying clinical decision making in coronary disease as well as the models for interpretation of the underlying physiological mechanisms. Presently, clinical decision making is based on non-invasive magnetic resonance imaging, MRI, of myocardial perfusion and invasive coronary hemodynamic measurements of coronary pressure and Doppler flow velocity signals obtained during catheterization. Within the euHeart project, several innovations have been developed and applied to improve diagnosis-based understanding of the underlying biophysical processes. Specifically, MRI perfusion data interpretation has been advanced by the gradientogram, a novel graphical representation of the spatiotemporal myocardial perfusion gradient. For hemodynamic data, functional indices of coronary stenosis severity that do not depend on maximal vasodilation are proposed and the Valsalva maneuver for indicating the extravascular resistance component of the coronary circulation has been introduced. Complementary to these advances, model innovation has been directed to the porous elastic model coupled to a one-dimensional model of the epicardial arteries. The importance of model development is related to the integration of information from different modalities, which in isolation often result in conflicting treatment recommendations.

Rem-GTPase regulates cardiac myocyte L-type calcium current.

RATIONALE: The L-type calcium channels (LTCC) are critical for maintaining Ca(2+)-homeostasis. In heterologous expression studies, the RGK-class of Ras-related G-proteins regulates LTCC function; however, the physiological relevance of RGK-LTCC interactions is untested. OBJECTIVE: In this report we test the hypothesis that the RGK protein, Rem, modulates native Ca(2+) current (I(Ca,L)) via LTCC in murine cardiomyocytes. METHODS AND RESULTS: Rem knockout mice (Rem(-/-)) were engineered, and I(Ca,L) and Ca(2+) -handling properties were assessed. Rem(-/-) ventricular cardiomyocytes displayed increased I(Ca,L) density. I(Ca,L) activation was shifted positive on the voltage axis, and ß-adrenergic stimulation normalized this shift compared with wild-type I(Ca,L). Current kinetics, steady-state inactivation, and facilitation was unaffected by Rem(-/-) . Cell shortening was not significantly different. Increased I(Ca,L) density in the absence of frank phenotypic differences motivated us to explore putative compensatory mechanisms. Despite the larger I(Ca,L) density, Rem(-/-) cardiomyocyte Ca(2+) twitch transient amplitude was significantly less than that compared with wild type. Computer simulations and immunoblot analysis suggests that relative dephosphorylation of Rem(-/-) LTCC can account for the paradoxical decrease of Ca(2+) transients. CONCLUSIONS: This is the first demonstration that loss of an RGK protein influences I(Ca,L) in vivo in cardiac myocytes.

A finite-element approach to the direct computation of relative cardiovascular pressure from time-resolved MR velocity data.

The evaluation of cardiovascular velocities, their changes through the cardiac cycle and the consequent pressure gradients has the capacity to improve understanding of subject-specific blood flow in relation to adjacent soft tissue movements. Magnetic resonance time-resolved 3D phase contrast velocity acquisitions (4D flow) represent an emerging technology capable of measuring the cyclic changes of large scale, multi-directional, subject-specific blood flow. A subsequent evaluation of pressure differences in enclosed vascular compartments is a further step which is currently not directly available from such data. The focus of this work is to address this deficiency through the development of a novel simulation workflow for the direct computation of relative cardiovascular pressure fields. Input information is provided by enhanced 4D flow data and derived MR domain masking. The underlying methodology shows numerical advantages in terms of robustness, global domain composition, the isolation of local fluid compartments and a treatment of boundary conditions. This approach is demonstrated across a range of validation examples which are compared with analytic solutions. Four subject-specific test cases are subsequently run, showing good agreement with previously published calculations of intra-vascular pressure differences. The computational engine presented in this work contributes to non-invasive access to relative pressure fields, incorporates the effects of both blood flow acceleration and viscous dissipation, and enables enhanced evaluation of cardiovascular blood flow.

Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter.

Parameter estimation from non-invasive measurements is a crucial step in patient-specific cardiac modeling. It also has the potential to provide significant assistance in the clinical diagnosis of cardiac diseases through the quantification of myocardial material heterogeneity. In this paper, we formulate a novel Reduced-order Unscented Kalman Filter (rUKF) applied to the left ventricular (LV) nonlinear mechanical model based on cubic-Hermite finite elements. Material parameters in the widely-employed transversely isotropic Guccione's constitutive law are successfully identified for both homogeneous and heterogeneous cases. We conclude that the four parameters in Guccione's law can be uniquely and correctly determined in-silico from noisy displacement measurements of material points located on the myocardial surfaces. The future application of this novel and effective approach to real clinical measurements is thus promising.

An accurate, fast and robust method to generate patient-specific cubic Hermite meshes.

In-silico continuum simulations of organ and tissue scale physiology often require a discretisation or mesh of the solution domain. Cubic Hermite meshes provide a smooth representation of anatomy that is well-suited for simulating large deformation mechanics. Models of organ mechanics and deformation have demonstrated significant potential for clinical application. However, the production of a personalised mesh from patient's anatomy using medical images remains a major bottleneck in simulation workflows. To address this issue, we have developed an accurate, fast and automatic method for deriving patient-specific cubic Hermite meshes. The proposed solution customises a predefined template with a fast binary image registration step and a novel cubic Hermite mesh warping constructed using a variational technique. Image registration is used to retrieve the mapping field between the template mesh and the patient images. The variational warping technique then finds a smooth and accurate projection of this field into the basis functions of the mesh. Applying this methodology, cubic Hermite meshes are fitted to the binary description of shape with sub-voxel accuracy and within a few minutes, which is a significant advance over the existing state of the art methods. To demonstrate its clinical utility, a generic cubic Hermite heart biventricular model is personalised to the anatomy of four patients, and the resulting mechanical stability of these customised meshes is successfully demonstrated.

Verification of cardiac tissue electrophysiology simulators using an N-version benchmark.

Ongoing developments in cardiac modelling have resulted, in particular, in the development of advanced and increasingly complex computational frameworks for simulating cardiac tissue electrophysiology. The goal of these simulations is often to represent the detailed physiology and pathologies of the heart using codes that exploit the computational potential of high-performance computing architectures. These developments have rapidly progressed the simulation capacity of cardiac virtual physiological human style models; however, they have also made it increasingly challenging to verify that a given code provides a faithful representation of the purported governing equations and corresponding solution techniques. This study provides the first cardiac tissue electrophysiology simulation benchmark to allow these codes to be verified. The benchmark was successfully evaluated on 11 simulation platforms to generate a consensus gold-standard converged solution. The benchmark definition in combination with the gold-standard solution can now be used to verify new simulation codes and numerical methods in the future.

euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling.

The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.

Personalization of cubic Hermite meshes for efficient biomechanical simulations.

Cubic Hermite meshes provide an efficient representation of anatomy, and are useful for simulating soft tissue mechanics. However, their personalization can be a complex, time consuming and labour-intensive process. This paper presents a method based on image registration and using an existing template for deriving a patient-specific cubic Hermite mesh. Its key contribution is a solution to customise a Hermite continuous description of a shape with the use of a discrete warping field. Fitting accuracy is first tested and quantified against an analytical ground truth solution. To then demonstrate its clinical utility, a generic cubic Hermite heart ventricular model is personalized to the anatomy of a patient, and its mechanical stability is successfully tested. The method achieves an easy, fast and accurate personalization of cubic Hermite meshes, constituting a crucial step for the clinical adoption of physiological simulations.