A Fully-Coupled Electro-Mechanical Whole-Heart Computational Model: Influence of Cardiac Contraction on the ECG

The ECG is one of the most commonly used non-invasive tools to gain insights into the electrical functioning of the heart. It has been crucial as a foundation in the creation and validation of in silico models describing the underlying electrophysiological processes. However, so far, the contraction of the heart and its influences on the ECG have mainly been overlooked in in silico models. As the heart contracts and moves, so do the electrical sources within the heart responsible for the signal on the body surface, thus potentially altering the ECG. To illuminate these aspects, we developed a human 4-chamber electro-mechanically coupled whole heart in silico model and embedded it within a torso model. Our model faithfully reproduces measured 12-lead ECG traces, circulatory characteristics, as well as physiological ventricular rotation and atrioventricular valve plane displacement. We compare our dynamic model to three non-deforming ones in terms of standard clinically used ECG leads (Einthoven and Wilson) and body surface potential maps (BSPM). The non-deforming models consider the heart at its ventricular end-diastatic, end-diastolic and end-systolic states. The standard leads show negligible differences during P-Wave and QRS-Complex, yet during T-Wave the leads closest to the heart show prominent differences in amplitude. When looking at the BSPM, there are no notable differences during the P-Wave, but effects of cardiac motion can be observed already during the QRS-Complex, increasing further during the T-Wave. We conclude that for the modeling of activation (P-Wave/QRS-Complex), the associated effort of simulating a complete electro-mechanical approach is not worth the computational cost. But when looking at ventricular repolarization (T-Wave) in standard leads as well as BSPM, there are areas where the signal can be influenced by cardiac motion of the heart to an extent that should not be ignored.

Non-Rigid Image Registration based on Parameterized Surfaces: Application to 3D Cardiac Motion Image Analysis

This paper describes the Fast Radial Basis Function (RBF) method for cardiac motion tracking in 3D CT using non-rigid medical image registration based on parameterized (regular) surfaces. The technique is a point-based registration evaluation algorithm which does register 3D MR or CT images in real time. We first extract the surface of the whole heart 3D CT and its contrast enhanced part (left ventricle (LV) blood cavity) of each dataset with a semiautomatic contouring and a fully-automatic triangulation method followed by a global surface parameterization and optimization algorithm. In second step, a set of registration experiments are run to calculate the deformation field at various phases of cardiac motion or cycle from CT images, which results into significant deformation during each phase of a cycle. The surface points of the whole heart and LV are used to register the source systole image to various diastole target images taken at different phases during a heart beat. Our registration accuracy improves with the increase in number of salient feature points (i.e. optimized parameterized surfaces) and it has no effect on the speed of the algorithm (i.e. still less than a second). The results show that the target registration error is less than 3[Formula: see text]mm (2.53) and the performance of the Fast RBF algorithm is less than a second using a whole heart CT dataset of a single patient taken over the course of the entire cardiac cycle. At the end, the results for recovery (or analysis) of bigger deformation in heart CT images using the Fast RBF algorithm is compared to the state-of-the-art Free Form Deformation (FFD) registration technique. It is proved that the Fast RBF method is performing better in speed and slightly less accurate than the FFD (when measured in terms of NMI) due to iterative nature of the latter.

Value of cardiac magnetic resonance imaging derived spectral myocardial strain pattern for non-invasive diagnosis of myocarditis

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Society of Radiology European Institute for Biomedical Imaging Research Background Traditionally, cardiac function is quantified by measures of peak excursion, for example ejection fraction. However, myocardial strain estimation from cine- cardiac MRI allows quantification of cardiac motion over the whole heart cycle. We propose a spectral decomposition of the strain curves applying Discrete Fourier transformation (DFT). Purpose To evaluate a potential additive diagnostic value of spectral temporal strain curve quantification for non-invasive diagnosis of myocarditis using cardiac MRI. Methods In the single-center prospective study patients with suspected myocarditis underwent comprehensive cardiac MRI followed by biventricular endomyocardial biopsy (EMB) between 2012 and 2014. DFT was applied to myocardial strain curves extracted from cine-Images. As reference model, a L1- and L2-penalized logistic regression model using global native T1 time, T2 time and presence of late-gadolinium enhancement was trained to predict EMB results and compared to two models which additionally include three orders of DFT coefficients and ejection fraction, respectively. Predictive performance was evaluated in a tournament-leave-pair-out cross-validation approach with a bootstrap correction for testing of multiple hyperparameter configurations. Results Out of 100 patients (28 % female, median age 40 [IQR 32 to 56) years) with acute symptom-onset (<30 days) 65 had pathologically proven myocarditis in EMB. The DFT model showed best discrimination (Area under the receiver-operating-curve [AUC] 0.72 [95% CI 52 to 87]). Addition of ejection fraction (AUC 0.60 [95% CI: 0.43 to 0.74]) did not increase AUC compared to the reference (AUC 0.60 [95% CI: 0.43 to 0.74]). Posterior distribution of the bootstrap-corrected AUC difference between DFT and reference model was gaussian (mean 12%, standard deviation 12%) with a posterior probability of 86%, that DFT has a greater AUC. Conclusions Discrimination of myocarditis from similar clinical presentations remains challenging. The results support incremental discriminatory value of DFT-decomposed myocardial strain for non-invasive diagnosis of myocarditis. Future research should address the value of the spectral decomposition of cardiac motion trajectories in larger samples and different disease entities.