Phani Chinchapatnam

Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation.

Cardiac resynchronisation therapy (CRT) is an effective treatment for patients with congestive heart failure and a wide QRS complex. However, up to 30% of patients are non-responders to therapy in terms of exercise capacity or left ventricular reverse remodelling. A number of controversies still remain surrounding patient selection, targeted lead implantation and optimisation of this important treatment. The development of biophysical models to predict the response to CRT represents a potential strategy to address these issues. In this article, we present how the personalisation of an electromechanical model of the myocardium can predict the acute haemodynamic changes associated with CRT. In order to introduce such an approach as a clinical application, we needed to design models that can be individualised from images and electrophysiological mapping of the left ventricle. In this paper the personalisation of the anatomy, the electrophysiology, the kinematics and the mechanics are described. The acute effects of pacing on pressure development were predicted with the in silico model for several pacing conditions on two patients, achieving good agreement with invasive haemodynamic measurements: the mean error on dP/dt(max) is 47.5±35mmHgs(-1), less than 5% error. These promising results demonstrate the potential of physiological models personalised from images and electrophysiology signals to improve patient selection and plan CRT.

Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy

AIMS Cardiac resynchronization therapy (CRT) has emerged as one of the few effective and safe treatments for heart failure. However, identifying patients that will benefit from CRT remains controversial. The dependence of CRT efficacy on organ and cellular scale mechanisms was investigated in a patient-specific computer model to identify novel patient selection criteria. METHODS AND RESULTS A biophysically based patient-specific coupled electromechanics heart model has been developed which links the cellular and sub-cellular mechanisms which regulate cardiac function to the whole organ function observed clinically before and after CRT. A sensitivity analysis of the model identified lack of length dependence of tension regulation within the sarcomere as a significant contributor to the efficacy of CRT. Further simulation analysis demonstrated that in the whole heart, length-dependent tension development is key not only for the beat-to-beat regulation of stroke volume (Frank-Starling mechanism), but also the homogenization of tension development and strain. CONCLUSIONS In individuals with effective Frank-Starling mechanism, the length dependence of tension facilitates the homogenization of stress and strain. This can result in synchronous contraction despite asynchronous electrical activation. In these individuals, synchronizing electrical activation through CRT may have minimal benefit.

A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures

We describe a system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures. The technique uses a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan. We test a dynamic MRI sequence that acquires a small number of high resolution slices, rather than a single low resolution volume. Additionally, we use prior knowledge of the nature of cardiac respiratory motion by constraining the model to use only the dominant modes of motion. During the procedure the motion of the diaphragm is tracked in X-ray fluoroscopy images, allowing the roadmap to be updated using the motion model. X-ray image acquisition is cardiac gated. Validation is performed on four volunteer datasets and three patient datasets. The accuracy of the model in 3D was within 5mm in 97.6% of volunteer validations. For the patients, 2D accuracy was improved from 5 to 13mm before applying the model to 2-4mm afterwards. For the dynamic MRI sequence comparison, the highest errors were found when using the low resolution volume sequence with an unconstrained model.

Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia

In order to translate the important progress in cardiac electrophysiology modelling of the last decades into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and performance of the clinical procedures. This requires model personalization, i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Simplified macroscopic models can allow a rapid estimation of the tissue conductivity, but are often unreliable to predict arrhythmias. Conversely, complex biophysical models are more complete and have mechanisms of arrhythmogenesis and arrhythmia sustainibility, but are computationally expensive and their predictions at the organ scale still have to be validated. We present a coupled personalization framework that combines the power of the two kinds of models while keeping the computational complexity tractable. A simple eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell–Schaeffer (MS) model. Additional parameters related to action potential duration restitution curves for the tissue are further estimated for the MS model. This framework is applied to a clinical dataset derived from a hybrid X-ray/magnetic resonance imaging and non-contact mapping procedure on a patient with heart failure. This personalized MS model is then used to perform an in silico simulation of a ventricular tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling in order to both assess the risk of VT for a given patient and also to plan a potential subsequent radio-frequency ablation strategy to treat VT.

A Simultaneous X-Ray/MRI and Noncontact Mapping Study of the Acute Hemodynamic Effect of Left Ventricular Endocardial and Epicardial Cardiac Resynchronization Therapy in Humans

Background—Cardiac resynchronization therapy (CRT) using endocardial left ventricular (LV) pacing may be superior to conventional CRT. We studied the acute hemodynamic response to conventional CRT and LV pacing from different endocardial sites using a combined cardiac MRI and LV noncontact mapping (NCM) protocol to gain insights into the underlying mechanisms. Methods and Results—Fifteen patients (age, 63±10 years; 12 men) awaiting CRT were studied in a combined x-ray and MRI laboratory. Delayed-enhancement cardiac magnetic resonance was performed to define areas of myocardial fibrosis. Patients underwent an electrophysiological study incorporating endocardial and epicardial LV pacing. Acute hemodynamic response was measured using a pressure wire within the LV cavity to derive LV dP/dt max. NCM was used to define areas of slow conduction. There was a significant improvement in all LV pacing modes versus baseline (P<0.001). LV endocardial CRT from the best endocardial site was superior to conventional CRT, with a 79.8±49.0% versus 59.6±49.5% increase in LV dP/dt max of from baseline (P<0.05). The hemodynamic benefits of pacing were greater when LV stimulation was performed outside of areas of slow conduction defined by NCM (P<0.001). Delayed-enhancement cardiac magnetic resonance was able to delineate zones of slow conduction seen with NCM in ischemic patients but was unreliable in nonischemic patients. Conclusions—Endocardial LV pacing appears superior to conventional CRT, although the optimal site varies between subjects and is influenced by pacing within areas of slow conduction. Delayed-enhancement cardiac magnetic resonance was a poor predictor of zones of slow conduction in nonischemic patients.

An anisotropic multi-front fast marching method for real-time simulation of cardiac electrophysiology

Cardiac arrhythmias can develop complex electrophysiological patterns which complexify the planning and control of therapies, especially in the context of radio-frequency ablation. The development of electrophysiology models aims at testing different therapy strategies. However, current models are computationally expensive and often too complex to be adjusted with limited clinical data. In this paper, we propose a real-time method to simulate cardiac electrophysiology on triangular meshes. This model is based on a multi-front integration of the Fast Marching Method. This efficient approach opens new possibilities, including the ability to directly integrate modelling in the interventional room.

Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology

Biophysical models are increasingly used for medical applications at the organ scale. However, model predictions are rarely associated with a confidence measure although there are important sources of uncertainty in computational physiology methods. For instance, the sparsity and noise of the clinical data used to adjust the model parameters (personalization), and the difficulty in modeling accurately soft tissue physiology. The recent theoretical progresses in stochastic models make their use computationally tractable, but there is still a challenge in estimating patient-specific parameters with such models. In this work we propose an efficient Bayesian inference method for model personalization using polynomial chaos and compressed sensing. This method makes Bayesian inference feasible in real 3D modeling problems. We demonstrate our method on cardiac electrophysiology. We first present validation results on synthetic data, then we apply the proposed method to clinical data. We demonstrate how this can help in quantifying the impact of the data characteristics on the personalization (and thus prediction) results. Described method can be beneficial for the clinical use of personalized models as it explicitly takes into account the uncertainties on the data and the model parameters while still enabling simulations that can be used to optimize treatment. Such uncertainty handling can be pivotal for the proper use of modeling as a clinical tool, because there is a crucial requirement to know the confidence one can have in personalized models.

Relationship between endocardial activation sequences defined by high-density mapping to early septal contraction (septal flash) in patients with left bundle branch block undergoing cardiac resynchronization therapy

AIMS Early inward motion and thickening/thinning of the ventricular septum associated with left bundle branch block is known as the septal flash (SF). Correction of SF corresponds to response to cardiac resynchronization therapy (CRT). We hypothesized that SF was associated with a specific left ventricular (LV) activation pattern predicting a favourable response to CRT. We sought to characterize the spatio-temporal relationship between electrical and mechanical events by directly comparing non-contact mapping (NCM), acute haemodynamics, and echocardiography. METHODS AND RESULTS Thirteen patients (63 ± 10 years, 10 men) with severe heart failure (ejection fraction 22.8 ± 5.8%) awaiting CRT underwent echocardiography and NCM pre-implant. Presence and extent of SF defined visually and with M-mode was fused with NCM bulls eye plots of endocardial activation patterns. LV-dP/dt(max) was measured during different pacing modes. Five patients had a large SF, four small SF, and four no SF. Large SF patients had areas of conduction block in non-infarcted regions, whereas those with small or no SF did not. Patients with large SF had greater acute response to LV and biventricular (BIV) pacing vs. those with small/no SF (% increase dP/dt 28 ± 14 vs. 11 ± 19% for LV pacing and 42 ± 28 vs. 22 ± 21% for BIV pacing) (P < 0.05). This translated into a more favourable chronic response to CRT. The lines of conduction block disappeared with LV/BIV pacing while remaining with right ventricle pacing. CONCLUSION A strong association exists between electrical activation and mechanical deformation of the septum. Correction of both mechanical synchrony and the functional conduction block by CRT may explain the favourable response in patients with SF.

Model-Based Imaging of Cardiac Apparent Conductivity and Local Conduction Velocity for Diagnosis and Planning of Therapy

We present an adaptive algorithm which uses a fast electrophysiological (EP) model to estimate apparent electrical conductivity and local conduction velocity from noncontact mapping of the endocardial surface potential. Development of such functional imaging revealing hidden parameters of the heart can be instrumental for improved diagnosis and planning of therapy for cardiac arrhythmia and heart failure, for example during procedures such as radio-frequency ablation and cardiac resynchronisation therapy. The proposed model is validated on synthetic data and applied to clinical data derived using hybrid X-ray/magnetic resonance imaging. We demonstrate a qualitative match between the estimated conductivity parameter and pathology locations in the human left ventricle. We also present a proof of concept for an electrophysiological model which utilizes the estimated apparent conductivity parameter to simulate the effect of pacing different ventricular sites. This approach opens up possibilities to directly integrate modelling in the cardiac EP laboratory.

Toward Patient-Specific Myocardial Models of the Heart

This article presents a framework for building patient-specific models of the myocardium, to help diagnosis, therapy planning, and procedure guidance. The aim is to be able to introduce such models in clinical applications. Thus, there is a need to design models that can be adjusted from clinical data, images, or signals, which are sparse and noisy. The authors describe the three main components of a myocardial model: the anatomy, the electrophysiology, and the biomechanics. For each of these components, the authors try to obtain the best balance between prior knowledge and observable parameters to be able to adjust these models to patient data. To achieve this, there is a need to design models with the right level of complexity and a computational cost compatible with clinical constraints.