In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model

The solution of the inverse problem of electrocardiology allows the reconstruction of the spatial distribution of the electrical activity of the heart from the body surface electrocardiogram (electrocardiographic imaging, ECGI). ECGI using the equivalent dipole layer (EDL) model has shown to be accurate for cardiac activation times. However, validation of this method to determine repolarization times is lacking. In the present study, we determined the accuracy of the EDL model in reconstructing cardiac repolarization times, and assessed the robustness of the method under less ideal conditions (addition of noise and errors in tissue conductivity). A monodomain model was used to determine the transmembrane potentials in three different excitationrepolarization patterns (sinus beat and ventricular ectopic beats) as the gold standard. These were used to calculate the body surface ECGs using a finite element model. The resulting body surface electrograms (ECGs) were used as input for the EDLbased inverse reconstruction of repolarization times. The reconstructed repolarization times correlated well (COR > 0.85) with the gold standard, with almost no decrease in correlation after adding errors in tissue conductivity of the model or noise to the body surface ECG. Therefore, ECGI using the EDL model allows adequate reconstruction of cardiac repolarization times

Brain and Human Body Modelling 2021

This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book

OpenMEEG: opensource software for quasistatic bioelectromagnetics

Background: Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to presen

Evaluation and Correction of B1+-Based Brain Subject-Specific SAR Maps Using Electrical Properties Tomography

The specific absorption rate (SAR) estimates the amount of power absorbed by the tissue and is determined by the electrical conductivity and the E-field. Conductivity can be estimated using Electric Properties Tomography (EPT) but only the E-field component associated with B-1(+) can be deduced from B-1- mapping. Herein, a correction factor was calculated to compensate for the differences between the actual SAR and the one obtained with B-1(+). Numerical simulations were performed for 27 head mod-els at 128 MHz. Ground-truth local-SAR and 10g-SAR (SAR(GT)) were computed using the exact electrical conductivity and the E-field. Estimated local-SAR and 10g-SAR (SAR(EST)) were com-puted using the electrical conductivity obtained with a convection-reaction EPT and the E-field obtained from B-1(+). Correction factors (CFs) were estimated for gray matter, white matter, and cere-brospinal fluid (CSF). A comparison was performed for different levels of signal-to-noise ratios (SNR). Local-SAR/10g-SAR CF was 3.08 +/- 0/06 / 2.11 +/- 0.04 for gray matter, 1.79 +/- 0/05 / 2.06 +/- 0.04 for white matter, and 2.59 +/- 0/05 / 1.95 +/- 0.03 for CSF. SAR(EST) without CF were underestimated (ratio across [infinity -25] SNRs: 0.52 +/- 0.02 for local-SAR; 0.55 +/- 0.01 for 10g-SAR). After cor-rection, SAREST was equivalent to SAR(GT) (ratio across [infinity -25] SNRs: 0.97 +/- 0.02 for local-SAR; 1.06 +/- 0.01 for 10g-SAR). SAR maps based on B-1(+) can be corrected with a correction factor to compensate for potential differences between the actual SAR and the SAR calculated with the E-field derived from B-1(+)

Quantitative comparisons of forward problems in MEEG.

This document gives comparisons between several methods that solve the forward problem in MEEG by comparing their precision on a 3-layer spherical model. These methods are based on finite elements which either use surfacic meshes with triangles, volumic meshes with tetrahedra, or implicit elements deduced from levelsets