Noninvasive Electrocardiographic Imaging (ECGi) to Guide Catheter Ablation of Scar-related Ventricular Tachycardia

Scar-related VT is caused by local \textit{short circuits} of electrical propagation formed by slow-conducting channels of surviving tissue within a scar. Catheter ablation treats scar-related VT by destroying the critical channel of surviving tissues. Its efficacy heavily relies on how well the channels critical to the formation of VT circuits can be localized. Unfortunately, in current practice, this relies on invasive catheter mapping that falls short in several critical aspects: up to 90$\%$ of the VT circuits are too short-lived to be mapped, the mapping cannot be done non-invasively prior to the ablation procedure, and the mapping is restricted to one heart surface at a time. Electrocardiographic imaging (ECGi) is a noninvasive approach that reconstructs cardiac electrical signals from a very dense body surface electrocardiogram (ECG) in combination with patient-specific geometries of the heart and torso. In this dissertation, we investigate the clinical utility of ECGi in guiding catheter ablation of scar-related VT. Specifically, we investigate two open questions that are not well-understood in the potential of ECGi for mapping VT circuits. First, instead of commonly-used epicardial ECGi, we investigate the validity of simultaneous epicardial and endocardial ECGi mapping of VT circuits, and the possibility of using information from these two surfaces to infer the morphology of 3D circuits. Second, we investigate the integration of ECGi electrical information of VT circuits with magnetic resonance imaging (MRI) of scar analysis for joint electrical and structural delineation of the substrates for VT circuits. These studies were performed on a combination of computer simulation, animal model, and human subject data. Experimental results showed that epi-endo ECGi mapping could reconstruct VT circuits, differentiate 2D versus 3D circuits, and provide information about the location of the VT circuit beneath the surface. They also showed that integrated MRI-ECGi analysis offered a quantitative characterization of the scar substrate that forms a VT circuit. These outcomes showed that simultaneous epi-endo ECGi in the combination of MRI structural scar imaging may provide a viable augmentation to the current practice of invasive catheter mapping. It may help clinicians plan for the ablation prior to the procedure by equipping them with knowledge about a VT circuit\u27s critical components, the surfaces that are involved, and the 3D morphology of the VT circuit

Investigating left ventricular infarct extension after myocardial infarction using cardiac imaging and patient-specific modelling

Acute myocardial infarction (MI) is one of the leading causes of death worldwide that commonly affects the left ventricle (LV). Following MI, the LV mechanical loading is altered and may undergo a maladaptive compensatory mechanism that progressively leads to adverse LV remodelling and then heart failure. One of the remodelling processes is the infarct extension which involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. A recent personalized LV modelling work found that infarct extension was correlated to inadequate diastolic fibre stretch and higher infarct stiffness. However, other possible factors of infarct extension may not have been elucidated in this work due to the limited number of myocardial locations analysed at the subendocardium only. Using human patient-specific left- ventricular (LV) models established from cardiac magnetic resonance imaging (MRI) of 6 MI patients, the correlation between infarct extension and regional mechanics impairment was studied. Prior to the modelling, a 2D-4D registration-cum-segmentation framework for the delineation of LV in late gadolinium enhanced (LGE) MRI was first developed, which is a pre-requisite for infarct scar quantification and localization in patient-specific 3D LV models. This framework automatically corrects for motion artifacts in multimodal MRI scans, resolving the issue of inaccurate infarct mapping and geometry reconstruction which is typically done manually in most patient-specific modelling work. The registration framework was evaluated against cardiac MRI data from 27 MI patients and showed high accuracy and robustness in delineating LV in LGE MRI of various quality and different myocardial features. This framework allows the integration of LV data from both LGE and cine scans and to facilitate the reconstruction of accurate 3D LV and infarct geometries for subsequent computational study. In the patient-specific LV mechanical modelling, the LV mechanics were formulated using a quasi-static and nearly incompressible hyperelastic material law with transversely isotropic behaviour. The patient-specific models were incorporated with realistic fibre orientation and excitable contracting myocardium. Optimisation of passive and active material parameters were done by minimizing the myocardial wall distance between the reference and end-diastole/end-systole LV geometries. Full cardiac cycle of the LV models was then simulated and stress/strain data were extracted to determine the correlation between regional mechanics abnormality and infarct extension. The fibre stress-strain loops (FSSLs) were analysed and its abnormality was characterized using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. It was found that infarct extension was correlated to severely abnormal FSSL in the form of counter-clockwise loop, as indicated by negative DREW values. In regions demonstrating negative DREW values, substantial isovolumic relaxation (IVR) fibre stretching was observed. Further analysis revealed that the occurrence of severely abnormal FSSL near the RZ-BZ boundary was due to a large amount of surrounding infarcted tissue that worsen with excessively stiff IZ

Prog Biophys Mol Biol

Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy.DP1 HL123271/HL/NHLBI NIH HHS/United StatesDP1HL123271/DP/NCCDPHP CDC HHS/United StatesR01 HL103428/HL/NHLBI NIH HHS/United StatesR01-HL103428/HL/NHLBI NIH HHS/United States2015-08-19T00:00:00Z25148771PMC425386

The role of cardiac MRI in the management of ventricular arrhythmias in ischaemic and non-ischaemic dilated cardiomyopathy

Ventricular tachycardia (VT) and VF account for the majority of sudden cardiac deaths worldwide. Treatments for VT/VF include anti-arrhythmic drugs, ICDs and catheter ablation, but these treatments vary in effectiveness and carry substantial risks and/or expense. Current methods of selecting patients for ICD implantation are imprecise and fail to identify some at-risk patients, while leading to others being overtreated. In this article, the authors discuss the current role and future direction of cardiac MRI (CMRI) in refining diagnosis and personalising ventricular arrhythmia management. The capability of CMRI with gadolinium contrast delayed-enhancement patterns and, more recently, T1 mapping to determine the aetiology of patients presenting with heart failure is well established. Although CMRI imaging in patients with ICDs can be challenging, recent technical developments have started to overcome this. CMRI can contribute to risk stratification, with precise and reproducible assessment of ejection fraction, quantification of scar and ‘border zone’ volumes, and other indices. Detailed tissue characterisation has begun to enable creation of personalised computer models to predict an individual patient’s arrhythmia risk. When patients require VT ablation, a substrate-based approach is frequently employed as haemodynamic instability may limit electrophysiological activation mapping. Beyond accurate localisation of substrate, CMRI could be used to predict the location of re-entrant circuits within the scar to guide ablation

Personalized Multi-Scale Modeling of the Atria: Heterogeneities, Fiber Architecture, Hemodialysis and Ablation Therapy

This book targets three fields of computational multi-scale cardiac modeling. First, advanced models of the cellular atrial electrophysiology and fiber orientation are introduced. Second, novel methods to create patient-specific models of the atria are described. Third, applications of personalized models in basic research and clinical practice are presented. The results mark an important step towards the patient-specific model-based atrial fibrillation diagnosis, understanding and treatment

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Objective: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. Methods: A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. Results: Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively. Discussion: Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images

High-Resolution Whole-Heart Imaging and Modeling for Studying Cardiac Arrhythmia

Cardiac arrhythmia is a life-threatening heart rhythm disorder affecting millions of people worldwide. The underlying structure of the heart plays an important role in cardiac activity and could promote rhythm disorders. Accurate knowledge of whole-heart cardiac geometry and microstructure in normal and disease hearts is essential for a complete understanding of the mechanisms of arrhythmias. This dissertation presents novel structural data at the whole-heart level aimed at advancing knowledge of cardiac structure in normal and infarcted hearts, and at constructing whole-heart computational models. A 3D diffusion tensor MRI (DTMRI) technique was implemented on a clinical scanner to image intact large animal and human hearts with high image quality and spatial resolution ex vivo. This method was first applied to reconstruct the 3D myofiber organization in 8 human atria nondestructively and at submillimeter resolution. The findings showed that the main features of atrial anatomy are mostly preserved across subjects despite variability in the exact location and orientation of the bundles. Further, we were able to cluster, visualize, and characterize the distinct major bundles in the human atria. Quantitative analysis of the fiber angles across the atrial wall revealed that the transmural fiber angle distribution is heterogeneous throughout the atria. We next studied microstructural remodeling in infarcted porcine and human hearts by combining DTMRI with high-resolution Late Gadolinium Enhancement imaging. This enabled us to provide reconstructions of both fiber architecture and scar distribution in infarcted hearts with an unprecedented level of detail, and to systematically quantify the transmural pattern of diffusion eigenvector orientation. Our results demonstrated that the fiber orientation is generally preserved inside the scar but at a higher transmural gradient of inclination angle. Lastly, we employed the obtained data to generate whole-heart computational models of infarcted hearts with detailed scar geometry and subject-specific fiber orientation. We used these models in simulations to investigate the contribution of the infarct microarchitecture to ventricular tachycardia. The simulation results showed that the reentry circuits traverse thin viable tissues with complex geometries located inside of the infarct. The high resolution of the images enabled 3D reconstruction and characterization of such structures