Quantitative assessment of myocardial ischemia by electrocardiographic and scintigraphic imaging

Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach

Noninvasive cardiac electrophysiological (EP) imaging aims to mathematically reconstruct the spatiotemporal dynamics of cardiac sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, a fixed-model approach enforces the source distribution to follow a pre-assumed structure that does not always match the varying spatiotemporal distribution of actual sources. To understand the model-data relation and examine the impact of prior models, we present a multiple-model approach for volumetric cardiac EP imaging where multiple prior models are included and automatically picked by the available ECG data. Multiple models are incorporated as an Lp-norm prior for sources, where p is an unknown hyperparameter with a prior uniform distribution. To examine how different combinations of models may be favored by different measurement data, the posterior distribution of cardiac sources and hyperparameter p is calculated using a Markov Chain Monte Carlo (MCMC) technique. The importance of multiple-model prior was assessed in two sets of synthetic and real-data experiments, compared to fixed-model priors (using Laplace and Gaussian priors). The results showed that the posterior combination of models (the posterior distribution of p) as determined by the ECG data differed substantially when reconstructing sources with different sizes and structures. While the use of fixed models is best suited in situations where the prior assumption fits the actual source structures, the use of an automatically adaptive set of models may have the ability to better address model-data mismatch and to provide consistent performance in reconstructing sources with different properties.

Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges

Electrical activity at the level of the heart muscle can be noninvasively reconstructed from body-surface electrocardiograms (ECGs) and patient-specific torso-heart geometry. This modality, coined electrocardiographic imaging, could fill the gap between the noninvasive (low-resolution) 12-lead ECG and invasive (high-resolution) electrophysiology studies. Much progress has been made to establish electrocardiographic imaging, and clinical studies appear with increasing frequency. However, many assumptions and model choices are involved in its execution, and only limited validation has been performed. In this article, we will discuss the technical details, clinical applications and current limitations of commonly used methods in electrocardiographic imaging. It is important for clinicians to realise the influence of certain assumptions and model choices for correct and careful interpretation of the results. This, in combination with more extensive validation, will allow for exploitation of the full potential of noninvasive electrocardiographic imaging as a powerful clinical tool to expedite diagnosis, guide therapy and improve risk stratification.

Noninvasive transmural electrophysiological imaging based on minimization of total-variation functional

While tomographic imaging of cardiac structure and kinetics has improved substantially, electrophysiological mapping of the heart is still restricted to the surface with little or no depth information beneath. The progress in reconstructing 3-D action potential from surface voltage data has been hindered by the intrinsic ill-posedness of the problem and the lack of a unique solution in the absence of prior assumptions. In this work, we propose a novel adaption of the total-variation (TV) prior to exploit the unique spatial property of transmural action potential of being piecewise smooth with a steep boundary (gradient) separating depolarized and repolarized regions. We present a variational TV-prior instead of a common discrete TV-prior for improved robustness to mesh resolution, and solve the TV-minimization by a sequence of weighted, first-order L2-norm minimization. In a large set of phantom experiments, the proposed method is shown to outperform existing quadratic methods in preserving the steep gradient of action potential along the border of infarcts, as well as in capturing the disruption to the normal path of electrical wavefronts. Real-data experiments also further demonstrate the potential of the proposed method in revealing the location and shape of infarcts when quadratic methods fail to do so.

Hierarchical multiple-model Bayesian approach to transmural electrophysiological imaging

Noninvasive electrophysiological (EP) imaging of the heart aims to mathematically reconstruct the spatiotemporal dynamics of cardiac current sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, this approach enforces the source distribution to follow a preassumed spatial structure that does not always match the varying spatiotemporal distribution of current sources. We propose a hierarchical Bayesian approach to transmural EP imaging that employs a continuous combination of multiple models, each reflecting a specific spatial property for current sources. Multiple models are incorporated as an Lp-norm prior for current sources, where p is an unknown hyperparameter with a prior probabilistic distribution. The current source estimation is obtained as an optimally weighted combination of solutions across all models, the weight being determined from the posterior distribution of p inferred from ECG data. The accuracy of our approach is assessed in a set of synthetic and real-data experiments on human heart-torso models. While the use of fixed models (L1- and L2-norm) only properly recovers sources with specific structures, our method delivers consistent performance in reconstructing sources with various extents and structures.

Lp-norm regularization in volumetric imaging of cardiac current sources

Advances in computer vision have substantially improved our ability to analyze the structure and mechanics of the heart. In comparison, our ability to observe and analyze cardiac electrical activities is much limited. The progress to computationally reconstruct cardiac current sources from noninvasive voltage data sensed on the body surface has been hindered by the ill-posedness and the lack of a unique solution of the reconstruction problem. Common L2- and L1-norm regularizations tend to produce a solution that is either too diffused or too scattered to reflect the complex spatial structure of current source distribution in the heart. In this work, we propose a general regularization with Lp-norm (1 < p < 2) constraint to bridge the gap and balance between an overly smeared and overly focal solution in cardiac source reconstruction. In a set of phantom experiments, we demonstrate the superiority of the proposed Lp-norm method over its L1 and L2 counterparts in imaging cardiac current sources with increasing extents. Through computer-simulated and real-data experiments, we further demonstrate the feasibility of the proposed method in imaging the complex structure of excitation wavefront, as well as current sources distributed along the postinfarction scar border. This ability to preserve the spatial structure of source distribution is important for revealing the potential disruption to the normal heart excitation.

Noninvasive computational imaging of cardiac electrophysiology for 3-D infarct

Myocardial infarction (MI) creates electrophysiologically altered substrates that are responsible for ventricular arrhythmias, such as tachycardia and fibrillation. The presence, size, location, and composition of infarct scar bear significant prognostic and therapeutic implications for individual subjects. We have developed a statistical physiological model-constrained framework that uses noninvasive body-surface-potential data and tomographic images to estimate subject-specific transmembrane-potential (TMP) dynamics inside the 3-D myocardium. In this paper, we adapt this framework for the purpose of noninvasive imaging, detection, and quantification of 3-D scar mass for postMI patients: the framework requires no prior knowledge of MI and converges to final subject-specific TMP estimates after several passes of estimation with intermediate feedback; based on the primary features of the estimated spatiotemporal TMP dynamics, we provide 3-D imaging of scar tissue and quantitative evaluation of scar location and extent. Phantom experiments were performed on a computational model of realistic heart-torso geometry, considering 87 transmural infarct scars of different sizes and locations inside the myocardium, and 12 compact infarct scars (extent between 10% and 30%) at different transmural depths. Real-data experiments were carried out on BSP and magnetic resonance imaging (MRI) data from four postMI patients, validated by gold standards and existing results. This framework shows unique advantage of noninvasive, quantitative, computational imaging of subject-specific TMP dynamics and infarct mass of the 3-D myocardium, with the potential to reflect details in the spatial structure and tissue composition/heterogeneity of 3-D infarct scar.

"Smart" liposomal nanocontainers in biology and medicine

The perspectives of using liposomes for delivery of drugs to desired parts of the human body have been intensively investigated for more than 30 years. During this time many inventions have been suggested and different kinds of liposomal devices developed, and a number of them have reached the stages of preclinical or clinical trials. The latest techniques can be used to develop biocompatible nano-sized liposomal containers having some abilities of artificial intellect, such as the presence of sensory and responsive units. However, only a few have been clinically approved. Further improvements in this area depend on our knowledge of the interactions of drugs with the lipid bilayer of liposomes. Further studies on liposomal transport through the human body, their targeting of cells requiring therapeutic treatment, and finally, the development of techniques for controlled drug delivery to desired acceptors on cell surfaces or in cytoplasm are still required.

The value of upsloping ST depression in diagnosing myocardial ischemia

We evaluated the value of upsloping ST-segment depression in predicting the severity of myocardial ischemia. Comparison of the exercise electrocardiographic changes was made to myocardial perfusion images and coronary angiograms as the criteria for ischemia. We retrospectively reviewed 621 patients who underwent exercise technetium-99m tetrofosmin single photon emission computed tomography (SPECT) for the assessment of suspected or known coronary artery disease followed by coronary angiography within a 3-month period. The test sensitivity and specificity of 1 mm horizontal or downsloping ST depression in predicting reversible ischemia as assessed by gated SPECT imaging (GSI) were 65% and 87%, respectively. The corresponding values were 67% and 94% compared to coronary angiography. The sensitivity and specificity of gated SPECT imaging compared to coronary angiography were 78% and 89%. On the other hand when 1 mm upsloping ST depression at 70 ms past the J-point was regarded as abnormal, along with horizontal and downsloping, the sensitivity and specificity were 82% and 90% compared to myocardial perfusion imaging, and 77% and 92% as assessed by coronary angiography. We conclude that upsloping ST-segment depression is associated with an increased risk of coronary artery disease and is a valuable predictor of myocardial ischemia.

The severity of myocardial ischemia can be predicted by the exercise electrocardiogram

The aim of the study was to evaluate the utility of exercise-induced ECG changes in predicting the degree of myocardial ischemia as compared to angiographic and nuclear scintigraphic images. Four hundred and seventy one patients in whom exercise nuclear scintigraphy and coronary angiography revealed significant coronary narrowing (> or =70% luminal diameter narrowing) were studied. Down-sloping ST depression was found to be the best predictor of a large area of reversible ischemia (RI). Time of resolution of ST depression was the second most powerful predictor and the time of onset of ST depression was the third best predictor. RI on nuclear scintigraphy correlated better than angiographic findings with the ECG changes.