Attacking the Inverse Electromagnetic Problem of the Heart with Computationally Compatible Anatomical and Histological Knowledge

Theofilogiannakos, Efstratios K.; Anogeianaki, A.; Klisarova, Anelia; Negrev, Negrin; Hatzitolios, Apostolos; Danias, P.G.; Anogianakis, G.

doi:10.1007/978-3-540-72375-2_6

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…Bayesian models are used so as to take into account uncertainties in the given information and to limit the results to those that are physiologically within the realm of possibility. Recent inverse reports that use a Bayesian approach include those of Greensite [98], Wang [99], [100], focusing on cardiac arrhythmias, and Efstratios [101], who integrated several sources of data. Bin He incorporated Bayesian concepts while focusing on visualizing cardiac arrhythmias [102].…”

Section: Bayesian Evaluation and Cardiologymentioning

confidence: 99%

Bayesian Quantitative Electrophysiology and Its Multiple Applications in Bioengineering

Barr

Nolte

Pollard

2010

IEEE Rev. Biomed. Eng.

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Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bio-engineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.

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Section: Bayesian Evaluation and Cardiologymentioning

confidence: 99%

Bayesian Quantitative Electrophysiology and Its Multiple Applications in Bioengineering

Barr

Nolte

Pollard

2010

IEEE Rev. Biomed. Eng.

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“…Successful as it is in providing adequate information for diagnosis and patient management, the sensitivity and specificity of the 12-lead electrocardiogram (ECG) may occasionally be suboptimal, as, for example, in the diagnosis of a posterior wall myocardial infarction (Perloff 1964) or of ischaemia and myocardial infarction in patients with left bundle branch block (Spiers 2007). In order to enhance the diagnostic value of the ECG, it is necessary to provide accurate solutions to the forward and inverse electrocardiographic problems (Theofilogiannakos et al 2007); whereby, for our purposes, the forward problem is defined as the computation of the electrical potential generated on the body surface by an embedded, known source. In a similar manner, we define the inverse problem as the determination of a heart-bound generator of an electrical field, on the basis of (a) the electrical potential that it produces on different points of the body surface and (b) the geometry of the volume conductor (i.e.…”

Section: Introductionmentioning

confidence: 99%

A semi-automated approach towards generating three-dimensional mesh of the heart using a hybrid MRI/histology database

Theofilogiannakos

Danias

et al. 2011

Computer Methods in Biomechanics and Biomedical Engineering

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Both the forward and inverse problems of electrocardiography rely on the precise modelling of the anatomic and electrical properties of the thoracic tissues. This, in turn, requires good knowledge of the electrical anisotropy as well as conductivity inhomogeneity of the heart, lungs and the rest of the thorax. Cardiac electrical anisotropy is related to its microstructure (fibre length, density and orientation). We hereby present detailed three-dimensional (3D) meshes of the thorax and heart, using image data from contiguous 2D magnetic resonance (MR) imaging slices as well as a realistic 3D cardiac fibre orientation model that derives its data from high-resolution ex vivo human heart MR images and from histology specimens of heart tissue. Using specific software, we integrated the 3D thorax and heart meshes in one that addresses the related modelling requirements for the solution of the forward and inverse problems of electrocardiography.

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“…The electromagnetic field that the heart generates is the result of numerous elementary dipolar excitations corresponding to the excitation of the individual myocardial fibers. Therefore, the knowledge of cardiac microstructure (fiber density and orientation) is necessary for an accurate description of the field source and a prerequisite for any large scale simulation of the electrical and mechanical behavior of the heart [6]. The cardiac anisotropy, which is directly related to its microstructure affects the generation of body surface potentials in two respects: first, the anisotropic conduction of the cardiac action potential and second, the anisotropic distribution of the electrical conductivity of the heart tissue [7].…”

Section: Introductionmentioning

confidence: 99%

A fiber orientation model of the human heart using classical histological methods, magnetic resonance imaging and interpolation techniques

Theofilogiannakos

Anogeianaki

et al. 2008

2008 Computers in Cardiology

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We present a cardiac model of post-mortem heart that can be applied to quantitative analysis of electrocardiologic problems. Two adult postmortem hearts in the systolic state were photographed and then were subjected to MRI scanning. Hence, anatomical slicing was performed using an automatic cutter that created 3mm thick sections. The first one dissected vertical to its longitudinal axis, and the second one at the sagittal plane. Each section was further diced into smaller specimens for further histological process. All the microscopic slides were digitized in order to be used for histological sections reconstructing. The need to define the fiber orientation led us to create a specific drawing package in MATLAB® called FiberCad. For each point of the extracted model, information of the electrical characteristics and the prevalent fibers' orientation can be accurately modeled.

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Attacking the Inverse Electromagnetic Problem of the Heart with Computationally Compatible Anatomical and Histological Knowledge

Cited by 3 publications

References 51 publications

Bayesian Quantitative Electrophysiology and Its Multiple Applications in Bioengineering

Bayesian Quantitative Electrophysiology and Its Multiple Applications in Bioengineering

A semi-automated approach towards generating three-dimensional mesh of the heart using a hybrid MRI/histology database

A fiber orientation model of the human heart using classical histological methods, magnetic resonance imaging and interpolation techniques

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