Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models.

This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.

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.

Inter-model consistency and complementarity: learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology.

Computational models of the heart at various scales and levels of complexity have been independently developed, parameterised and validated using a wide range of experimental data for over four decades. However, despite remarkable progress, the lack of coordinated efforts to compare and combine these computational models has limited their impact on the numerous open questions in cardiac physiology. To address this issue, a comprehensive dataset has previously been made available to the community that contains the cardiac anatomy and fibre orientations from magnetic resonance imaging as well as epicardial transmembrane potentials from optical mapping measured on a perfused ex-vivo porcine heart. This data was used to develop and customize four models of cardiac electrophysiology with different level of details, including a personalized fast conduction Purkinje system, a maximum a posteriori estimation of the 3D distribution of transmembrane potential, the personalization of a simplified reaction-diffusion model, and a detailed biophysical model with generic conduction parameters. This study proposes the integration of these four models into a single modelling and simulation pipeline, after analyzing their common features and discrepancies. The proposed integrated pipeline demonstrates an increase prediction power of depolarization isochrones in different pacing conditions.

Coupled personalisation of electrophysiology models for simulation of induced ischemic ventricular tachycardia.

Despite recent efforts in cardiac electrophysiology modelling, there is still a strong need to make macroscopic models usable in planning and assistance of the clinical procedures. This requires model personalisation i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Fast macroscopic models allow a quick estimation of the tissue conductivity, but are often unreliable in prediction of arrhythmias. On the other side, complex biophysical models are quite expensive for the tissue conductivity estimation, but are well suited for arrhythmia predictions. Here we present a coupled personalisation framework, which combines the benefits of the two models. A fast Eikonal (EK) 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 (APD) and APD restitution curves for the tissue are estimated for the MS model. This framework is applied to a clinical dataset provided with an hybrid X-Ray/MR imaging on an ischemic patient. This personalised MS Model is then used for in silico simulation of clinical Ventricular Tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling directly in the intervention room, in order to plan the radio-frequency ablation lines.

3D segmentation of the left ventricle combining long- and short-axis MR images.

OBJECTIVES: Segmentation of the left ventricle (LV) is required to quantify LV remodeling after myocardial infarction. Therefore spatiotemporal cine MR sequences including long-axis and short-axis images are acquired. In this paper a new segmentation method for fast and robust segmentation of the left ventricle is presented. METHODS: The new approach considers the position of the mitral valve and the apex as well as the long-axis contours to generate a 3D LV surface model. The segmentation result can be checked and adjusted in the short-axis images. Finally quantitative parameters were extracted. RESULTS: For evaluation the LV was segmented in eight datasets of the same subject by two medical experts using a contour drawing tool and the new segmentation tool. The results of both methods were compared concerning interaction time and intra- and inter-observer variance. The presented segmentation method proved to be fast. The mean difference and standard deviation of all parameters are decreased. In case of intra-observer comparison e.g. the mean ESV difference is reduced from 8.8% to 0.5%. CONCLUSION: A semi-automatic LV segmentation method has been developed that combines long- and short-axis views. Using the presented approach the intra- and interobserver difference as well as the time for the segmentation process are decreased. So the semi-automatic segmentation using long- and short-axis information proved to be fast and robust for the quantification of LV mass and volume properties.

Reproducible extraction of local and global parameters for functional analysis of the left ventricle in 4D MR image data.

OBJECTIVES: Left ventricle (LV) segmentation is required to quantify LV volume and mass parameters. Therefore, spatiotemporal Cine MR sequences in the short and long axis of the heart are acquired. Generally, LV segmentation methods consider short-axis sequences only. The reduced resolution in long-axis direction is one of the main reasons for inaccurate parameter extraction in the apical and basal area. The segmentation approach presented combines short- and long-axis information as well as motion tracking to enable the functional LV analysis in 4D MR Image Data. METHODS: First, anatomical landmarks like the mitral valve and the apex are defined in long-axis views in diastolic and systolic phase in order to specify the upper and lower boundary of the LV. Second, motion field approximation using non-linear registration enables the automatic contour propagation to all time points. Third, intersection planes are defined parallel to the mitral valve plane covering the whole ventricle. Finally, the 4D LV surface model is generated appending all in-plane contours. The segmentation results in short-axis images are checked and adjusted interactively and quantitative parameters are extracted. RESULTS: For evaluation the contours of 19 different datasets were traced by two medical experts using a contour drawing tool and the new segmentation tool. The results were compared to evaluate automatic contour propagation, robustness of the segmentation as well as interaction time. CONCLUSION: The automatic contour propagation enables the fast and reproducible generation of a 4D model for the functional analysis of the heart. The interaction time is decreased from approx. 60 minutes to 10 minutes per case. Inter- and intraobserver differences of the extracted parameters are decreased significantly.