712 Correlating Current Pathways With Myocardial Fiber Orientation Through Fusion of Data From Current Density and Diffusion Tensor Imaging

Foomany, Farbod Hosseyndoust; Beheshti, Mohammadali; Magtibay, Karl; Massé, Stéphane; Foltz, Warren D.; Sevaptsidis, Elias; Lai, Patrick F.H.; Nair, Kalyani; Jaffray, D.; Krishnan, Sridhar; Nanthakumar, K.; Umapathy, Karthikeyan

doi:10.1016/j.cjca.2012.07.645

“…Feature-Based Data Fusion -Canonical Correlation Analysis B ASED on our earlier attempts to fuse CDI and DTI data sets from previous experiments conducted on asystolic porcine hearts, it was found that there is some correlation between the components of CDI and DTI [57]. Further analysis of recent data sets (which were also used in this thesis) yielded results with significantly higher correlations as seen in Figure 4.1.…”

Section: Chaptermentioning

confidence: 81%

“…green channel contains anterior-posterior fibres; blue channel contains the superior-inferior fibres [57]. and Blue channel represents superior-inferior fibres DTI has been extensively used for white matter fibre tracking in the human brain to study an array of human brain pathologies such as stroke or schizophrenia [58].…”

Section: Diffusion Tensor Imagingmentioning

confidence: 99%

Fusing magnetic resonance imaging data for studying ventricular fibrillation

Magtibay¹

2023

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Ventricular Fibrillation (VF) has been described as seemingly random activations on the ventricles of the mammalian heart and is one of the causes of Sudden Cardiac Deaths (SCD). Medical imaging techniques, such as Magnetic Resonance Imaging (MRI), could potentially provide a better way of collecting data and understanding the true nature of VF than the techniques that are currently being employed. In addition, as there is a wide variety of MR techniques, fusing and jointly analyzing complementary data sets could also prove beneficial in providing parameters that are informative in studying VF and are otherwise unobservable by inspection. In this thesis, the author explores the quantification of the combination of MRI techniques, Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI), as novel tools for studying VF. This was accomplished by performing two feature-based data fusion techniques, Joint Independent Component Analysis (jICA) and Canonical Correlation Analysis (CCA). Using 12 imaging data sets from 10 live porcine heart experiments, both data fusion techniques provided unique ways from which the variations of CDI and DTI data sets were used to distinguish cardiac states. The results obtained by the jICA approach demonstrated discrimination between VF and non-VF subjects (p = 0:020) using the jICA loadings with evidence of a significant increase in the mutual information post fusion. For the CCA approach, using the pairwise mixing profiles, we observed discrimination between VF and non-VF subjects (p = 0:023) with a 7.25% increase in average correlation between the modalities, post fusion. The results of the study demonstrate that the fusion of CDI and DTI data sets captures and enhances the variations in electrical current pathways in relation to a myocardial structure that are unique to a cardiac state, such as VF. This study serves as a strong precursor for exploring MRI and data fusion techniques in studying VF. Such a study could provide greater insights on VF characteristics inspiring better treatment options for patients vulnerable to VF.

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“…green channel contains anterior-posterior fibres; blue channel contains the superior-inferior fibres [57]. and Blue channel represents superior-inferior fibres DTI has been extensively used for white matter fibre tracking in the human brain to study an array of human brain pathologies such as stroke or schizophrenia [58].…”

Section: Diffusion Tensor Imagingmentioning

confidence: 99%

Fusing magnetic resonance imaging data for studying ventricular fibrillation

Magtibay¹

2023

Preprint

0

View full text Add to dashboard Cite

Ventricular Fibrillation (VF) has been described as seemingly random activations on the ventricles of the mammalian heart and is one of the causes of Sudden Cardiac Deaths (SCD). Medical imaging techniques, such as Magnetic Resonance Imaging (MRI), could potentially provide a better way of collecting data and understanding the true nature of VF than the techniques that are currently being employed. In addition, as there is a wide variety of MR techniques, fusing and jointly analyzing complementary data sets could also prove beneficial in providing parameters that are informative in studying VF and are otherwise unobservable by inspection. In this thesis, the author explores the quantification of the combination of MRI techniques, Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI), as novel tools for studying VF. This was accomplished by performing two feature-based data fusion techniques, Joint Independent Component Analysis (jICA) and Canonical Correlation Analysis (CCA). Using 12 imaging data sets from 10 live porcine heart experiments, both data fusion techniques provided unique ways from which the variations of CDI and DTI data sets were used to distinguish cardiac states. The results obtained by the jICA approach demonstrated discrimination between VF and non-VF subjects (p = 0:020) using the jICA loadings with evidence of a significant increase in the mutual information post fusion. For the CCA approach, using the pairwise mixing profiles, we observed discrimination between VF and non-VF subjects (p = 0:023) with a 7.25% increase in average correlation between the modalities, post fusion. The results of the study demonstrate that the fusion of CDI and DTI data sets captures and enhances the variations in electrical current pathways in relation to a myocardial structure that are unique to a cardiac state, such as VF. This study serves as a strong precursor for exploring MRI and data fusion techniques in studying VF. Such a study could provide greater insights on VF characteristics inspiring better treatment options for patients vulnerable to VF.

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“…• Correlation of structural data obtained by DTI imaging and dynamic CDI data for the first time [73,74] • Improved post processing of the CDI imaging [75][76][77], and…”

Section: Proposed Approaches and Contributionsmentioning

confidence: 99%

Simulation models of current density imaging in studying cardiac states

Beheshti¹

2021

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Electro-mechanical disorders in cardiac function result in arrhythmias. Due to the non-stationary nature of these arrhythmias and, owing to lethality associated with certain type of arrhythmias, they are challenging to study. Most of the existing studies are limited in that they extract electrical activity from surface intracardiac electrical activity, either through the use of electrical or optical mapping. One way of studying current pathways inside and through biological tissues is by using Magnetic Resonance Imaging (MRI) based Low Frequency Current Density Imaging (LFCDI). For the first time CDI was used to study ex-vivo beating hearts in different cardiac states. It should be said that; this approach involves heavy logistical and procedural complexity, hence, it would be beneficial to adapt existing electrophysiological computer models to investigate and simulate current density maps specific to studying cardiac function. In achieving this, the proposed work presents an approach to model the current density maps in 3D and study the current distributions in different electrophysiological states of the heart. The structural and fiber orientation of the heart used in this study were extracted using MRI-based Diffusion Tensor Imaging. The monodomain and bidomain Aliev-Panfilov electrophysiological models were used for CDI modeling, and the results indicate that different states were distinguishable using range and correlation of simulated current density maps. The obtained results through modeling were corroborated with actual experimental CDI data from porcine hearts. Individually and comparatively, the experimental and simulation results for various states have the same trend in terms of variations (trend correlation coefficients ≥ 0.98) and state correlations (trend correlation coefficients ≥ 0.89). The results also show that the root mean square (RMS) error in average range ratios between bidomain CDI model results and real CDI data is 0.1972 and the RMS error in state correlations between bidomain CDI model results and real CDI data is 0.2833. These results indicate, as expected, the proposed bidomain model simulation of CDI corroborates well with experimental data and can serve as a valuable tool for studying lethal cardiac arrhythmias under different simulation conditions that are otherwise not possible or difficult in a real-world experimental setup.

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712 Correlating Current Pathways With Myocardial Fiber Orientation Through Fusion of Data From Current Density and Diffusion Tensor Imaging

Cited by 7 publications

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Fusing magnetic resonance imaging data for studying ventricular fibrillation

Fusing magnetic resonance imaging data for studying ventricular fibrillation

Fusing magnetic resonance imaging data for studying ventricular fibrillation

Simulation models of current density imaging in studying cardiac states

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