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.

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.

An analytical phantom for the evaluation of medical flow imaging algorithms.

Blood flow characteristics (e.g. velocity, pressure, shear stress, streamline and volumetric flow rate) are effective tools in diagnosis of cardiovascular diseases such as atherosclerotic plaque, aneurism and cardiac muscle failure. Noninvasive estimation of cardiovascular blood flow characteristics is mostly limited to the measurement of velocity components by medical imaging modalities. Once the velocity field is obtained from the images, other flow characteristics within the cardiovascular system can be determined using algorithms relating them to the velocity components. In this work, we propose an analytical flow phantom to evaluate these algorithms accurately. The Navier-Stokes equations are used to derive this flow phantom. The exact solution of these equations obtains analytical expression for the flow characteristics inside the domain. Features such as pulsatility, incompressibility and viscosity of flow are included in a three-dimensional domain. The velocity domain of the resulted system is presented as reference images. These images could be employed to evaluate the performance of different flow characteristic algorithms. In this study, we also present some applications of the obtained phantom. The calculation of pressure domain from velocity data, volumetric flow rate, wall shear stress and particle trace are the characteristics whose algorithms are evaluated here. We also present the application of this phantom in the analysis of noisy and low-resolution images. The presented phantom can be considered as a benchmark test to compare the accuracy of different flow characteristic algorithms.