Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI.

Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s)

Primer on Machine Learning in Electrophysiology

Artificial intelligence has become ubiquitous. Machine learning, a branch of artificial intelligence, leads the current technological revolution through its remarkable ability to learn and perform on data sets of varying types. Machine learning applications are expected to change contemporary medicine as they are brought into mainstream clinical practice. In the field of cardiac arrhythmia and electrophysiology, machine learning applications have enjoyed rapid growth and popularity. To facilitate clinical acceptance of these methodologies, it is important to promote general knowledge of machine learning in the wider community and continue to highlight the areas of successful application. The authors present a primer to provide an overview of common supervised (least squares, support vector machine, neural networks and random forest) and unsupervised (k-means and principal component analysis) machine learning models. The authors also provide explanations as to how and why the specific machine learning models have been used in arrhythmia and electrophysiology studies

Submillimeter diffusion tensor imaging and late gadolinium enhancement cardiovascular magnetic resonance of chronic myocardial infarction.

BackgroundKnowledge of the three-dimensional (3D) infarct structure and fiber orientation remodeling is essential for complete understanding of infarct pathophysiology and post-infarction electromechanical functioning of the heart. Accurate imaging of infarct microstructure necessitates imaging techniques that produce high image spatial resolution and high signal-to-noise ratio (SNR). The aim of this study is to provide detailed reconstruction of 3D chronic infarcts in order to characterize the infarct microstructural remodeling in porcine and human hearts.MethodsWe employed a customized diffusion tensor imaging (DTI) technique in conjunction with late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) on a 3T clinical scanner to image, at submillimeter resolution, myofiber orientation and scar structure in eight chronically infarcted porcine hearts ex vivo. Systematic quantification of local microstructure was performed and the chronic infarct remodeling was characterized at different levels of wall thickness and scar transmurality. Further, a human heart with myocardial infarction was imaged using the same DTI sequence.ResultsThe SNR of non-diffusion-weighted images was >100 in the infarcted and control hearts. Mean diffusivity and fractional anisotropy (FA) demonstrated a 43% increase, and a 35% decrease respectively, inside the scar tissue. Despite this, the majority of the scar showed anisotropic structure with FA higher than an isotropic liquid. The analysis revealed that the primary eigenvector orientation at the infarcted wall on average followed the pattern of original fiber orientation (imbrication angle mean: 1.96 ± 11.03° vs. 0.84 ± 1.47°, p = 0.61, and inclination angle range: 111.0 ± 10.7° vs. 112.5 ± 6.8°, p = 0.61, infarcted/control wall), but at a higher transmural gradient of inclination angle that increased with scar transmurality (r = 0.36) and the inverse of wall thickness (r = 0.59). Further, the infarcted wall exhibited a significant increase in both the proportion of left-handed epicardial eigenvectors, and in the angle incoherency. The infarcted human heart demonstrated preservation of primary eigenvector orientation at the thinned region of infarct, consistent with the findings in the porcine hearts.ConclusionsThe application of high-resolution DTI and LGE-CMR revealed the detailed organization of anisotropic infarct structure at a chronic state. This information enhances our understanding of chronic post-infarction remodeling in large animal and human hearts

Degradation of T-Tubular Microdomains and Altered cAMP Compartmentation Lead to Emergence of Arrhythmogenic Triggers in Heart Failure Myocytes: An in silico Study

Heart failure (HF) is one of the most common causes of morbidity and mortality worldwide. Although many patients suffering from HF die from sudden cardiac death caused by arrhythmias, the mechanism linking HF remodeling to an increased arrhythmogenic propensity remains incomplete. HF is typically characterized by a progressive loss of transverse tubule (T-tubule) domains, which leads to an altered distribution of L-type calcium channels (LTCCs). Microdomain degradation also causes the disruption of the β2 adrenergic receptor (β2AR) and phosphodiesterase (PDE) signaling localization, normally confined to the dyadic space. The goal of this study was to analyze how these subcellular changes affect the function of LTCCs and lead to the emergence of ventricular cell-level triggers of arrhythmias. To accomplish this, we developed a novel computational model of a human ventricular HF myocyte in which LTCCs were divided into six different populations, based on their location and signaling environment they experience. To do so, we included T-tubular microdomain remodeling which led to a subset of LTCCs to be redistributed from the T-tubular to the surface membrane and allowed for different levels of phosphorylation of LTCCs by PKA, based on the presence of β2ARs and PDEs. The model was used to study the behavior of the LTCC current (ICaL) under basal and sympathetic stimulation and its effect on cellular action potential. Our results showed that channels redistributed from the T-tubular membrane to the bulk of the sarcolemma displayed an altered function in their new, non-native signaling domain. Incomplete calcium dependent inactivation, which resulted in a longer-lasting and larger-in-magnitude LTCC current, was observed when we decoupled LTCCs from ryanodine receptors and removed them from the dyadic space. The magnitude of the LTCC current, especially in the surface sarcolemma, was also increased via phosphorylation by the redistributed β2ARs and PDEs. These changes in LTCC current led to the development of early afterdepolarizations. Thus, our study shows that altered LTCC function is a potential cause for the emergence of cell-level triggers of arrhythmia, and that β2ARs and PDEs present useful therapeutic targets for treatment of HF and prevention of sudden cardiac death