MCG simulations with a realistic heart-torso model

The magnetocardiogram

The magnetic field produced by the heart's electrical activity is called the magnetocardiogram (MCG). The first 20 years of MCG research established most of the concepts, instrumentation, and computational algorithms in the field. Additional insights into fundamental mechanisms of biomagnetism were gained by studying isolated hearts or even isolated pieces of cardiac tissue. Much effort has gone into calculating the MCG using computer models, including solving the inverse problem of deducing the bioelectric sources from biomagnetic measurements. Recently, most magnetocardiographic research has focused on clinical applications, driven in part by new technologies to measure weak biomagnetic fields.

A method for magnetocardiography functional localization based on boundary element method and Nelder-Mead simplex algorithm

BACKGROUND

The magnetocardiography (MCG) functional localization can transfer the biomagnetic signal to the electrical activity information inside the heart. The electrical activity is directly related to the physiological function of the heart.

METHODS

This study proposes a practical method for MCG functional localization based on the boundary element method (BEM) and the Nelder-Mead (NM) simplex algorithm. Single equivalent moving current dipole (SEMCD) is served as the equivalent cardiac source. The parameters of SEMCD are adapted using the NM simplex algorithm by fitting the measured MCG with the calculated MCG obtained based on BEM. The SEMCD parameters are solved in the sense that the difference between measured and calculated MCG is minimized.

RESULTS

The factors affecting the localization accuracy of this BEM-NM method were first explored with synthetic signals. Then, the results with real MCG signals show a good agreement between the SEMCD location and the region where ventricle depolarization starts, demonstrating the feasibility of this idea.

CONCLUSIONS

This is the first three-dimensional localization of the onset of ventricular depolarization with the BEM-NM method. The method is promising in the noninvasive localization of lesions for heart diseases.

3D Heart Simulation And Recognition Of Various Events

This paper presents a new way to solve the inverse problem of electrocardiography in terms of heart model parameters. The developed event estimation and recognition method is based on an optimization system of heart model parameters. An ANN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm. The optimal model parameters were determined by minimizing the objective functions, as relations of the observed and model-generated body surface ECGs. The final evaluation results, validated by physicians were about 86% correct. Starting from the fact that input ECGs contained various malfunction cases, such as Wolff-Parkinson-White (WPW) syndrome, atrial and ventricular fibrillation, these results suggest that this approach provides a robust inverse solution, circumventing most of the difficulties of the ECG inverse problem.

Finite element modelling and simulations in cardiovascular mechanics and cardiology: A bibliography 1993–2004

The paper gives a bibliographical review of the finite element modelling and simulations in cardiovascular mechanics and cardiology from the theoretical as well as practical points of views. The bibliography lists references to papers, conference proceedings and theses/dissertations that were published between 1993 and 2004. At the end of this paper, more than 890 references are given dealing with subjects as: Cardiovascular soft tissue modelling; material properties; mechanisms of cardiovascular components; blood flow; artificial components; cardiac diseases examination; surgery; and other topics.

The effect of volume currents due to myocardial anisotropy on body surface potentials

Changes in anterior and posterior body surface potential maps (BSPMs) due to myocardial anisotropy were examined using a highly heterogeneous finite element model of an adult male subject constructed from segmented magnetic resonance images. A total of 23 different tissue types were identified in the whole torso. The myocardial fibre orientations in the human heart wall were mapped from the fibre orientations of a canine heart which are available in the literature using deformable mapping techniques. The current and potential distributions in the whole torso were computed using dipolar sources in the septum, apical area, left ventricular wall or right ventricular wall. For each dipole x, y, z orientations were studied. An adaptive finite element solver was used to compute currents and potential distributions in the whole torso with an element size of 0.78 x 0.78 x 3 mm in the myocardium and larger elements in other parts of the torso. For each dipole position two cases were studied. In one case the myocardium was isotropic and in the other it was anisotropic. It was found that BSPMs showed a very notable difference between the isotropic and the anisotropic myocardium for all dipole positions with the largest difference for the apical dipoles. The correlation coefficients for the BSPMs between the isotropic and anisotropic cases ranged from 0.83 for an apical dipole to 0.99 for an RV wall dipole. These results suggest that myocardial fibre anisotropy plays an important role in determining the body surface potentials.

Effect of myocardial anisotropy on the torso current flow patterns, potentials and magnetic fields

The effects of myocardial anisotropy on the torso current flow patterns, voltage and the magnetic field were examined using an anatomically realistic torso model of an adult male subject. A finite element model of the torso was built with 19 major tissue types identified. The myocardial fibre orientation in the heart wall was included with a voxel resolution of 0.078 x 0.078 x 0.3 cm. The fibre orientations from the canine heart which are available in the literature were mapped to our adult male subject's human heart using deformable mapping techniques. The current and potential distribution in the whole torso were computed using an idealized dipolar source of +/-1.0 V in the middle of the septum of the heart wall as a boundary condition. An adaptive finite element solver was used. Two cases were studied. In one case the myocardium was isotropic and in the other it was anisotropic. It was found that the current density distribution shows a very noticeable difference between the isotropic and anisotropic myocardium. The resultant magnetic field in front of the torso was computed using the Biot-Savart law. It was found that the magnetic field profile was slightly affected by the myocardial anisotropy. The potential on the torso surface also shows noticeable changes due to the myocardial anisotropy.

Cardiac biomagnetic source estimation with a heart-torso model and a trained neural network

The intensity of the cardiac sources for normal adult subjects was estimated from given magnetic field profiles with a trained neural network based on the relationship of the electrical activity of the heart to the cardiac magnetic fields. The input for training the neural network consisted of the magnetic field profiles above the torso during the heartbeat. The outputs were the dipole intensities which produced those magnetic field profiles. A back propagating algorithm with bias and momentum was utilized for training. The measured and simulated torso magnetic field profiles and magnetocardiograms were used for training the neural network. Estimation of the dipole intensities was performed for unknown magnetic field profiles with the trained neural network. The estimated cardiac dipole intensities were reasonably close to the true dipole intensities. These results show the feasibility of the estimation of cardiac dipole intensities with a trained neural network under a very restricted forward model of the cardiac magnetic fields. Generalization of the results to cover a large population base could be difficult because the activation isochrones are different from subject to subject.