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Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.

Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.

Marine derived long chain polyunsaturated fatty acids (PUFAs) were found to have benefits in reducing inducibility and maintenance of atrial fibrillation (AF) in a dog model. This study was conducted to evaluate the effect of PUFAs on local atrial electrical conduction properties acquired via a multi-electrode plaque sutured to the posterior wall of the left atrium of the heart in these dogs. Eleven dogs underwent simultaneous atrioventricular pacing (SAVP) for 2 weeks, and were organized into 2 groups: 5 dogs received no PUFAs (SAVP-PLACEBO), 6 dogs received Eicosapentaenoic or Docosahexaenoic acid derived from fish oils (SAVP-PUFA), where PUFAs were given for 21 days, starting 1 week prior to pacing and during the 2 week pacing period. Three features were extracted, which were the average conduction velocity, average intra atrial conduction time, and total activation time. The PUFA group had a faster average conduction velocity (0.82±0.19 m/s) than the PLACEBO group (0.47±0.21 m/s, P=0.02). Using the average conduction velocity feature, classification was performed with a linear classifier and leave-one-out method. In the SAVP-PLACEBO group, 60% of the dogs were correctly classified, and 66% of the dogs were correctly classified in SAVP-PUFA group, leading to an overall classification accuracy of 63.5%.

Low frequency current density imaging (LFCDI) is a magnetic resonance imaging (MRI) technique which enables calculation of current pathways within the medium of study. The induced current produces a magnetic flux which presents itself in phase images obtained through MRI scanning. A class of LFCDI challenges arises from the subject rotation requirement, which calls for reliability analysis metrics and specific image registration techniques. In this study these challenges are formulated and in light of proposed discussions, the reliability analysis of calculation of current pathways in a designed phantom and a pig heart is presented. The current passed is measured with less than 5% error for phantom, using CDI method. It is shown that Gauss's law for magnetism can be treated as reliability metric in matching the images in two orientations. For the phantom and pig heart the usefulness of image registration for mitigation of rotation errors is demonstrated. The reliability metric provides a good representation of the degree of correspondence between images in two orientations for phantom and pig heart. In our CDI experiments this metric produced values of 95% and 26%, for phantom, and 88% and 75% for pig heart, for mismatch rotations of 0 and 20 degrees respectively.

Ventricular Fibrillation (VF) is a cardiac arrhythmia for which the only available treatment option is defibrillation by electrical shock. Existing literature indicates that VF could be the manifestation of different sources controlling the heart with different degrees of organization. In this work we test the hypothesis that the pre-shock waveforms of successful and unsuccessful shock outcomes could be related to the number of independent sources present in these waveforms. The proposed method uses Blind Source Separation (BSS) to extract independent components in frequency direction from a pig database consisting of 20 pre-shock waveforms. The slope of the energy capture curve was used as an indicator to demonstrate the number of independent sources required to model the pre-shock waveforms. The results were also quantified by performing a linear discriminant analysis based classification achieving an overall classification accuracy of 75%. The results indicate that successful cases can be modeled with less number of independent sources compared to unsuccessful cases.

During cardiac resuscitation from ventricular fibrillation (VF) it would be helpful if we could monitor and predict the optimal state of the heart to be shocked into a perfusing rhythm. Real-time feedback of this state to the emergency medical staff (EMS) could improve the survival rate after resuscitation. In this paper, using real world out-of-the-hospital human VF data obtained during resuscitation by EMS personnel, we present the results of applying wavelet markers in predicting the shock outcomes. We also performed comparative analysis of 5 existing techniques (spectral and correlation based approaches) against the proposed wavelet markers. A database of 29 human VF tracings was extracted from the defibrillator recordings collected by the EMS personnel and was used to validate the waveform markers. The results obtained by the comparison of the wavelet based features with other spectral, and correlation-based features indicates that the proposed wavelet features perform well with an overall accuracy of 79.3% in predicting the shock outcomes and hence demonstrate potential to provide near real-time feedback to EMS personnel in optimizing resuscitation outcomes.